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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
lorien/grab
|
2d170c31a3335c2e29578b42a5d62ef3efc5d7ee
|
grab/util/cookies.py
|
MockRequest.origin_req_host
|
(self)
|
return self.get_origin_req_host()
| 102 | 103 |
def origin_req_host(self) -> str:
return self.get_origin_req_host()
|
https://github.com/lorien/grab/blob/2d170c31a3335c2e29578b42a5d62ef3efc5d7ee/project1/grab/util/cookies.py#L102-L103
| 1 |
[
0,
1
] | 100 |
[] | 0 | true | 81.927711 | 2 | 1 | 100 | 0 |
def origin_req_host(self) -> str:
return self.get_origin_req_host()
| 216 |
||
lorien/grab
|
2d170c31a3335c2e29578b42a5d62ef3efc5d7ee
|
grab/util/cookies.py
|
MockRequest.host
|
(self)
|
return self.get_host()
| 106 | 107 |
def host(self) -> str:
return self.get_host()
|
https://github.com/lorien/grab/blob/2d170c31a3335c2e29578b42a5d62ef3efc5d7ee/project1/grab/util/cookies.py#L106-L107
| 1 |
[
0
] | 50 |
[
1
] | 50 | false | 81.927711 | 2 | 1 | 50 | 0 |
def host(self) -> str:
return self.get_host()
| 217 |
||
lorien/grab
|
2d170c31a3335c2e29578b42a5d62ef3efc5d7ee
|
grab/util/cookies.py
|
MockResponse.__init__
|
(self, headers: HTTPMessage | HTTPHeaderDict)
|
Make a MockResponse for `cookielib` to read.
:param headers: a httplib.HTTPMessage or analogous carrying the headers
|
Make a MockResponse for `cookielib` to read.
| 118 | 123 |
def __init__(self, headers: HTTPMessage | HTTPHeaderDict) -> None:
"""Make a MockResponse for `cookielib` to read.
:param headers: a httplib.HTTPMessage or analogous carrying the headers
"""
self._headers = headers
|
https://github.com/lorien/grab/blob/2d170c31a3335c2e29578b42a5d62ef3efc5d7ee/project1/grab/util/cookies.py#L118-L123
| 1 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 81.927711 | 6 | 1 | 100 | 3 |
def __init__(self, headers: HTTPMessage | HTTPHeaderDict) -> None:
self._headers = headers
| 218 |
|
lorien/grab
|
2d170c31a3335c2e29578b42a5d62ef3efc5d7ee
|
grab/util/cookies.py
|
MockResponse.info
|
(self)
|
return self._headers
| 125 | 126 |
def info(self) -> HTTPMessage | HTTPHeaderDict:
return self._headers
|
https://github.com/lorien/grab/blob/2d170c31a3335c2e29578b42a5d62ef3efc5d7ee/project1/grab/util/cookies.py#L125-L126
| 1 |
[
0,
1
] | 100 |
[] | 0 | true | 81.927711 | 2 | 1 | 100 | 0 |
def info(self) -> HTTPMessage | HTTPHeaderDict:
return self._headers
| 219 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
calc_grid_map_config
|
(ox, oy, xyreso)
|
return minx, miny, maxx, maxy, xw, yw
| 18 | 26 |
def calc_grid_map_config(ox, oy, xyreso):
minx = round(min(ox) - EXTEND_AREA / 2.0)
miny = round(min(oy) - EXTEND_AREA / 2.0)
maxx = round(max(ox) + EXTEND_AREA / 2.0)
maxy = round(max(oy) + EXTEND_AREA / 2.0)
xw = int(round((maxx - minx) / xyreso))
yw = int(round((maxy - miny) / xyreso))
return minx, miny, maxx, maxy, xw, yw
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L18-L26
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 93.421053 | 9 | 1 | 100 | 0 |
def calc_grid_map_config(ox, oy, xyreso):
minx = round(min(ox) - EXTEND_AREA / 2.0)
miny = round(min(oy) - EXTEND_AREA / 2.0)
maxx = round(max(ox) + EXTEND_AREA / 2.0)
maxy = round(max(oy) + EXTEND_AREA / 2.0)
xw = int(round((maxx - minx) / xyreso))
yw = int(round((maxy - miny) / xyreso))
return minx, miny, maxx, maxy, xw, yw
| 440 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
atan_zero_to_twopi
|
(y, x)
|
return angle
| 43 | 48 |
def atan_zero_to_twopi(y, x):
angle = math.atan2(y, x)
if angle < 0.0:
angle += math.pi * 2.0
return angle
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L43-L48
| 2 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 93.421053 | 6 | 2 | 100 | 0 |
def atan_zero_to_twopi(y, x):
angle = math.atan2(y, x)
if angle < 0.0:
angle += math.pi * 2.0
return angle
| 441 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
precasting
|
(minx, miny, xw, yw, xyreso, yawreso)
|
return precast
| 51 | 75 |
def precasting(minx, miny, xw, yw, xyreso, yawreso):
precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)]
for ix in range(xw):
for iy in range(yw):
px = ix * xyreso + minx
py = iy * xyreso + miny
d = math.hypot(px, py)
angle = atan_zero_to_twopi(py, px)
angleid = int(math.floor(angle / yawreso))
pc = precastDB()
pc.px = px
pc.py = py
pc.d = d
pc.ix = ix
pc.iy = iy
pc.angle = angle
precast[angleid].append(pc)
return precast
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L51-L75
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24
] | 100 |
[] | 0 | true | 93.421053 | 25 | 4 | 100 | 0 |
def precasting(minx, miny, xw, yw, xyreso, yawreso):
precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)]
for ix in range(xw):
for iy in range(yw):
px = ix * xyreso + minx
py = iy * xyreso + miny
d = math.hypot(px, py)
angle = atan_zero_to_twopi(py, px)
angleid = int(math.floor(angle / yawreso))
pc = precastDB()
pc.px = px
pc.py = py
pc.d = d
pc.ix = ix
pc.iy = iy
pc.angle = angle
precast[angleid].append(pc)
return precast
| 442 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
generate_ray_casting_grid_map
|
(ox, oy, xyreso, yawreso)
|
return pmap, minx, maxx, miny, maxy, xyreso
| 78 | 103 |
def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso):
minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
pmap = [[0.0 for i in range(yw)] for i in range(xw)]
precast = precasting(minx, miny, xw, yw, xyreso, yawreso)
for (x, y) in zip(ox, oy):
d = math.hypot(x, y)
angle = atan_zero_to_twopi(y, x)
angleid = int(math.floor(angle / yawreso))
gridlist = precast[angleid]
ix = int(round((x - minx) / xyreso))
iy = int(round((y - miny) / xyreso))
for grid in gridlist:
if grid.d > d:
pmap[grid.ix][grid.iy] = 0.5
pmap[ix][iy] = 1.0
return pmap, minx, maxx, miny, maxy, xyreso
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L78-L103
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25
] | 100 |
[] | 0 | true | 93.421053 | 26 | 6 | 100 | 0 |
def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso):
minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
pmap = [[0.0 for i in range(yw)] for i in range(xw)]
precast = precasting(minx, miny, xw, yw, xyreso, yawreso)
for (x, y) in zip(ox, oy):
d = math.hypot(x, y)
angle = atan_zero_to_twopi(y, x)
angleid = int(math.floor(angle / yawreso))
gridlist = precast[angleid]
ix = int(round((x - minx) / xyreso))
iy = int(round((y - miny) / xyreso))
for grid in gridlist:
if grid.d > d:
pmap[grid.ix][grid.iy] = 0.5
pmap[ix][iy] = 1.0
return pmap, minx, maxx, miny, maxy, xyreso
| 443 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
draw_heatmap
|
(data, minx, maxx, miny, maxy, xyreso)
| 106 | 110 |
def draw_heatmap(data, minx, maxx, miny, maxy, xyreso):
x, y = np.mgrid[slice(minx - xyreso / 2.0, maxx + xyreso / 2.0, xyreso),
slice(miny - xyreso / 2.0, maxy + xyreso / 2.0, xyreso)]
plt.pcolor(x, y, data, vmax=1.0, cmap=plt.cm.Blues)
plt.axis("equal")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L106-L110
| 2 |
[
0
] | 20 |
[
1,
3,
4
] | 60 | false | 93.421053 | 5 | 1 | 40 | 0 |
def draw_heatmap(data, minx, maxx, miny, maxy, xyreso):
x, y = np.mgrid[slice(minx - xyreso / 2.0, maxx + xyreso / 2.0, xyreso),
slice(miny - xyreso / 2.0, maxy + xyreso / 2.0, xyreso)]
plt.pcolor(x, y, data, vmax=1.0, cmap=plt.cm.Blues)
plt.axis("equal")
| 444 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
main
|
()
| 113 | 133 |
def main():
print(__file__ + " start!!")
xyreso = 0.25 # x-y grid resolution [m]
yawreso = np.deg2rad(10.0) # yaw angle resolution [rad]
for i in range(5):
ox = (np.random.rand(4) - 0.5) * 10.0
oy = (np.random.rand(4) - 0.5) * 10.0
pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(
ox, oy, xyreso, yawreso)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso)
plt.plot(ox, oy, "xr")
plt.plot(0.0, 0.0, "ob")
plt.pause(1.0)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L113-L133
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
] | 71.428571 |
[] | 0 | false | 93.421053 | 21 | 3 | 100 | 0 |
def main():
print(__file__ + " start!!")
xyreso = 0.25 # x-y grid resolution [m]
yawreso = np.deg2rad(10.0) # yaw angle resolution [rad]
for i in range(5):
ox = (np.random.rand(4) - 0.5) * 10.0
oy = (np.random.rand(4) - 0.5) * 10.0
pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(
ox, oy, xyreso, yawreso)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso)
plt.plot(ox, oy, "xr")
plt.plot(0.0, 0.0, "ob")
plt.pause(1.0)
| 445 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
precastDB.__init__
|
(self)
| 31 | 37 |
def __init__(self):
self.px = 0.0
self.py = 0.0
self.d = 0.0
self.angle = 0.0
self.ix = 0
self.iy = 0
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L31-L37
| 2 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 93.421053 | 7 | 1 | 100 | 0 |
def __init__(self):
self.px = 0.0
self.py = 0.0
self.d = 0.0
self.angle = 0.0
self.ix = 0
self.iy = 0
| 446 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/raycasting_grid_map/raycasting_grid_map.py
|
precastDB.__str__
|
(self)
|
return str(self.px) + "," + str(self.py) + "," + str(self.d) + "," + str(self.angle)
| 39 | 40 |
def __str__(self):
return str(self.px) + "," + str(self.py) + "," + str(self.d) + "," + str(self.angle)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/raycasting_grid_map/raycasting_grid_map.py#L39-L40
| 2 |
[
0
] | 50 |
[
1
] | 50 | false | 93.421053 | 2 | 1 | 50 | 0 |
def __str__(self):
return str(self.px) + "," + str(self.py) + "," + str(self.d) + "," + str(self.angle)
| 447 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
kmeans_clustering
|
(rx, ry, nc)
|
return clusters
| 19 | 34 |
def kmeans_clustering(rx, ry, nc):
clusters = Clusters(rx, ry, nc)
clusters.calc_centroid()
pre_cost = float("inf")
for loop in range(MAX_LOOP):
print("loop:", loop)
cost = clusters.update_clusters()
clusters.calc_centroid()
d_cost = abs(cost - pre_cost)
if d_cost < DCOST_TH:
break
pre_cost = cost
return clusters
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L19-L34
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15
] | 100 |
[] | 0 | true | 95.604396 | 16 | 3 | 100 | 0 |
def kmeans_clustering(rx, ry, nc):
clusters = Clusters(rx, ry, nc)
clusters.calc_centroid()
pre_cost = float("inf")
for loop in range(MAX_LOOP):
print("loop:", loop)
cost = clusters.update_clusters()
clusters.calc_centroid()
d_cost = abs(cost - pre_cost)
if d_cost < DCOST_TH:
break
pre_cost = cost
return clusters
| 448 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
calc_raw_data
|
(cx, cy, n_points, rand_d)
|
return rx, ry
| 85 | 93 |
def calc_raw_data(cx, cy, n_points, rand_d):
rx, ry = [], []
for (icx, icy) in zip(cx, cy):
for _ in range(n_points):
rx.append(icx + rand_d * (random.random() - 0.5))
ry.append(icy + rand_d * (random.random() - 0.5))
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L85-L93
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 95.604396 | 9 | 3 | 100 | 0 |
def calc_raw_data(cx, cy, n_points, rand_d):
rx, ry = [], []
for (icx, icy) in zip(cx, cy):
for _ in range(n_points):
rx.append(icx + rand_d * (random.random() - 0.5))
ry.append(icy + rand_d * (random.random() - 0.5))
return rx, ry
| 449 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
update_positions
|
(cx, cy)
|
return cx, cy
| 96 | 108 |
def update_positions(cx, cy):
# object moving parameters
DX1 = 0.4
DY1 = 0.5
DX2 = -0.3
DY2 = -0.5
cx[0] += DX1
cy[0] += DY1
cx[1] += DX2
cy[1] += DY2
return cx, cy
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L96-L108
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 95.604396 | 13 | 1 | 100 | 0 |
def update_positions(cx, cy):
# object moving parameters
DX1 = 0.4
DY1 = 0.5
DX2 = -0.3
DY2 = -0.5
cx[0] += DX1
cy[0] += DY1
cx[1] += DX2
cy[1] += DY2
return cx, cy
| 450 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
main
|
()
| 111 | 145 |
def main():
print(__file__ + " start!!")
cx = [0.0, 8.0]
cy = [0.0, 8.0]
n_points = 10
rand_d = 3.0
n_cluster = 2
sim_time = 15.0
dt = 1.0
time = 0.0
while time <= sim_time:
print("Time:", time)
time += dt
# objects moving simulation
cx, cy = update_positions(cx, cy)
raw_x, raw_y = calc_raw_data(cx, cy, n_points, rand_d)
clusters = kmeans_clustering(raw_x, raw_y, n_cluster)
# for animation
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
clusters.plot_cluster()
plt.plot(cx, cy, "or")
plt.xlim(-2.0, 10.0)
plt.ylim(-2.0, 10.0)
plt.pause(dt)
print("Done")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L111-L145
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
33,
34
] | 92.592593 |
[] | 0 | false | 95.604396 | 35 | 3 | 100 | 0 |
def main():
print(__file__ + " start!!")
cx = [0.0, 8.0]
cy = [0.0, 8.0]
n_points = 10
rand_d = 3.0
n_cluster = 2
sim_time = 15.0
dt = 1.0
time = 0.0
while time <= sim_time:
print("Time:", time)
time += dt
# objects moving simulation
cx, cy = update_positions(cx, cy)
raw_x, raw_y = calc_raw_data(cx, cy, n_points, rand_d)
clusters = kmeans_clustering(raw_x, raw_y, n_cluster)
# for animation
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
clusters.plot_cluster()
plt.plot(cx, cy, "or")
plt.xlim(-2.0, 10.0)
plt.ylim(-2.0, 10.0)
plt.pause(dt)
print("Done")
| 451 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
Clusters.__init__
|
(self, x, y, n_label)
| 39 | 47 |
def __init__(self, x, y, n_label):
self.x = x
self.y = y
self.n_data = len(self.x)
self.n_label = n_label
self.labels = [random.randint(0, n_label - 1)
for _ in range(self.n_data)]
self.center_x = [0.0 for _ in range(n_label)]
self.center_y = [0.0 for _ in range(n_label)]
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L39-L47
| 2 |
[
0,
1,
2,
3,
4,
5,
7,
8
] | 88.888889 |
[] | 0 | false | 95.604396 | 9 | 4 | 100 | 0 |
def __init__(self, x, y, n_label):
self.x = x
self.y = y
self.n_data = len(self.x)
self.n_label = n_label
self.labels = [random.randint(0, n_label - 1)
for _ in range(self.n_data)]
self.center_x = [0.0 for _ in range(n_label)]
self.center_y = [0.0 for _ in range(n_label)]
| 452 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
Clusters.plot_cluster
|
(self)
| 49 | 52 |
def plot_cluster(self):
for label in set(self.labels):
x, y = self._get_labeled_x_y(label)
plt.plot(x, y, ".")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L49-L52
| 2 |
[
0
] | 25 |
[
1,
2,
3
] | 75 | false | 95.604396 | 4 | 2 | 25 | 0 |
def plot_cluster(self):
for label in set(self.labels):
x, y = self._get_labeled_x_y(label)
plt.plot(x, y, ".")
| 453 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
Clusters.calc_centroid
|
(self)
| 54 | 59 |
def calc_centroid(self):
for label in set(self.labels):
x, y = self._get_labeled_x_y(label)
n_data = len(x)
self.center_x[label] = sum(x) / n_data
self.center_y[label] = sum(y) / n_data
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L54-L59
| 2 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 95.604396 | 6 | 2 | 100 | 0 |
def calc_centroid(self):
for label in set(self.labels):
x, y = self._get_labeled_x_y(label)
n_data = len(x)
self.center_x[label] = sum(x) / n_data
self.center_y[label] = sum(y) / n_data
| 454 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
Clusters.update_clusters
|
(self)
|
return cost
| 61 | 77 |
def update_clusters(self):
cost = 0.0
for ip in range(self.n_data):
px = self.x[ip]
py = self.y[ip]
dx = [icx - px for icx in self.center_x]
dy = [icy - py for icy in self.center_y]
dist_list = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)]
min_dist = min(dist_list)
min_id = dist_list.index(min_dist)
self.labels[ip] = min_id
cost += min_dist
return cost
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L61-L77
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16
] | 100 |
[] | 0 | true | 95.604396 | 17 | 5 | 100 | 0 |
def update_clusters(self):
cost = 0.0
for ip in range(self.n_data):
px = self.x[ip]
py = self.y[ip]
dx = [icx - px for icx in self.center_x]
dy = [icy - py for icy in self.center_y]
dist_list = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)]
min_dist = min(dist_list)
min_id = dist_list.index(min_dist)
self.labels[ip] = min_id
cost += min_dist
return cost
| 455 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/kmeans_clustering/kmeans_clustering.py
|
Clusters._get_labeled_x_y
|
(self, target_label)
|
return x, y
| 79 | 82 |
def _get_labeled_x_y(self, target_label):
x = [self.x[i] for i, label in enumerate(self.labels) if label == target_label]
y = [self.y[i] for i, label in enumerate(self.labels) if label == target_label]
return x, y
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/kmeans_clustering/kmeans_clustering.py#L79-L82
| 2 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 95.604396 | 4 | 3 | 100 | 0 |
def _get_labeled_x_y(self, target_label):
x = [self.x[i] for i, label in enumerate(self.labels) if label == target_label]
y = [self.y[i] for i, label in enumerate(self.labels) if label == target_label]
return x, y
| 456 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
main
|
()
| 255 | 263 |
def main():
print("start!!")
test_position_set()
test_polygon_set()
plt.show()
print("done!!")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L255-L263
| 2 |
[
0
] | 11.111111 |
[
1,
3,
4,
6,
8
] | 55.555556 | false | 77.037037 | 9 | 1 | 44.444444 | 0 |
def main():
print("start!!")
test_position_set()
test_polygon_set()
plt.show()
print("done!!")
| 457 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.__init__
|
(self, width, height, resolution,
center_x, center_y, init_val=0.0)
|
__init__
:param width: number of grid for width
:param height: number of grid for heigt
:param resolution: grid resolution [m]
:param center_x: center x position [m]
:param center_y: center y position [m]
:param init_val: initial value for all grid
|
__init__
| 18 | 39 |
def __init__(self, width, height, resolution,
center_x, center_y, init_val=0.0):
"""__init__
:param width: number of grid for width
:param height: number of grid for heigt
:param resolution: grid resolution [m]
:param center_x: center x position [m]
:param center_y: center y position [m]
:param init_val: initial value for all grid
"""
self.width = width
self.height = height
self.resolution = resolution
self.center_x = center_x
self.center_y = center_y
self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution
self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution
self.ndata = self.width * self.height
self.data = [init_val] * self.ndata
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L18-L39
| 2 |
[
0,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21
] | 59.090909 |
[] | 0 | false | 77.037037 | 22 | 1 | 100 | 8 |
def __init__(self, width, height, resolution,
center_x, center_y, init_val=0.0):
self.width = width
self.height = height
self.resolution = resolution
self.center_x = center_x
self.center_y = center_y
self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution
self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution
self.ndata = self.width * self.height
self.data = [init_val] * self.ndata
| 458 |
|
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.get_value_from_xy_index
|
(self, x_ind, y_ind)
|
get_value_from_xy_index
when the index is out of grid map area, return None
:param x_ind: x index
:param y_ind: y index
|
get_value_from_xy_index
| 41 | 55 |
def get_value_from_xy_index(self, x_ind, y_ind):
"""get_value_from_xy_index
when the index is out of grid map area, return None
:param x_ind: x index
:param y_ind: y index
"""
grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind)
if 0 <= grid_ind < self.ndata:
return self.data[grid_ind]
else:
return None
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L41-L55
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13
] | 93.333333 |
[
14
] | 6.666667 | false | 77.037037 | 15 | 2 | 93.333333 | 6 |
def get_value_from_xy_index(self, x_ind, y_ind):
grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind)
if 0 <= grid_ind < self.ndata:
return self.data[grid_ind]
else:
return None
| 459 |
|
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.get_xy_index_from_xy_pos
|
(self, x_pos, y_pos)
|
return x_ind, y_ind
|
get_xy_index_from_xy_pos
:param x_pos: x position [m]
:param y_pos: y position [m]
|
get_xy_index_from_xy_pos
| 57 | 68 |
def get_xy_index_from_xy_pos(self, x_pos, y_pos):
"""get_xy_index_from_xy_pos
:param x_pos: x position [m]
:param y_pos: y position [m]
"""
x_ind = self.calc_xy_index_from_position(
x_pos, self.left_lower_x, self.width)
y_ind = self.calc_xy_index_from_position(
y_pos, self.left_lower_y, self.height)
return x_ind, y_ind
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L57-L68
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11
] | 100 |
[] | 0 | true | 77.037037 | 12 | 1 | 100 | 4 |
def get_xy_index_from_xy_pos(self, x_pos, y_pos):
x_ind = self.calc_xy_index_from_position(
x_pos, self.left_lower_x, self.width)
y_ind = self.calc_xy_index_from_position(
y_pos, self.left_lower_y, self.height)
return x_ind, y_ind
| 460 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.set_value_from_xy_pos
|
(self, x_pos, y_pos, val)
|
return flag
|
set_value_from_xy_pos
return bool flag, which means setting value is succeeded or not
:param x_pos: x position [m]
:param y_pos: y position [m]
:param val: grid value
|
set_value_from_xy_pos
| 70 | 87 |
def set_value_from_xy_pos(self, x_pos, y_pos, val):
"""set_value_from_xy_pos
return bool flag, which means setting value is succeeded or not
:param x_pos: x position [m]
:param y_pos: y position [m]
:param val: grid value
"""
x_ind, y_ind = self.get_xy_index_from_xy_pos(x_pos, y_pos)
if (not x_ind) or (not y_ind):
return False # NG
flag = self.set_value_from_xy_index(x_ind, y_ind, val)
return flag
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L70-L87
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
14,
15,
16,
17
] | 94.444444 |
[
13
] | 5.555556 | false | 77.037037 | 18 | 3 | 94.444444 | 7 |
def set_value_from_xy_pos(self, x_pos, y_pos, val):
x_ind, y_ind = self.get_xy_index_from_xy_pos(x_pos, y_pos)
if (not x_ind) or (not y_ind):
return False # NG
flag = self.set_value_from_xy_index(x_ind, y_ind, val)
return flag
| 461 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.set_value_from_xy_index
|
(self, x_ind, y_ind, val)
|
set_value_from_xy_index
return bool flag, which means setting value is succeeded or not
:param x_ind: x index
:param y_ind: y index
:param val: grid value
|
set_value_from_xy_index
| 89 | 108 |
def set_value_from_xy_index(self, x_ind, y_ind, val):
"""set_value_from_xy_index
return bool flag, which means setting value is succeeded or not
:param x_ind: x index
:param y_ind: y index
:param val: grid value
"""
if (x_ind is None) or (y_ind is None):
return False, False
grid_ind = int(y_ind * self.width + x_ind)
if 0 <= grid_ind < self.ndata:
self.data[grid_ind] = val
return True # OK
else:
return False
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L89-L108
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
12,
13,
14,
15,
16,
17,
18,
19
] | 95 |
[
11
] | 5 | false | 77.037037 | 20 | 4 | 95 | 7 |
def set_value_from_xy_index(self, x_ind, y_ind, val):
if (x_ind is None) or (y_ind is None):
return False, False
grid_ind = int(y_ind * self.width + x_ind)
if 0 <= grid_ind < self.ndata:
self.data[grid_ind] = val
return True # OK
else:
return False
| 462 |
|
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.set_value_from_polygon
|
(self, pol_x, pol_y, val, inside=True)
|
set_value_from_polygon
Setting value inside or outside polygon
:param pol_x: x position list for a polygon
:param pol_y: y position list for a polygon
:param val: grid value
:param inside: setting data inside or outside
|
set_value_from_polygon
| 110 | 135 |
def set_value_from_polygon(self, pol_x, pol_y, val, inside=True):
"""set_value_from_polygon
Setting value inside or outside polygon
:param pol_x: x position list for a polygon
:param pol_y: y position list for a polygon
:param val: grid value
:param inside: setting data inside or outside
"""
# making ring polygon
if (pol_x[0] != pol_x[-1]) or (pol_y[0] != pol_y[-1]):
np.append(pol_x, pol_x[0])
np.append(pol_y, pol_y[0])
# setting value for all grid
for x_ind in range(self.width):
for y_ind in range(self.height):
x_pos, y_pos = self.calc_grid_central_xy_position_from_xy_index(
x_ind, y_ind)
flag = self.check_inside_polygon(x_pos, y_pos, pol_x, pol_y)
if flag is inside:
self.set_value_from_xy_index(x_ind, y_ind, val)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L110-L135
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25
] | 100 |
[] | 0 | true | 77.037037 | 26 | 6 | 100 | 8 |
def set_value_from_polygon(self, pol_x, pol_y, val, inside=True):
# making ring polygon
if (pol_x[0] != pol_x[-1]) or (pol_y[0] != pol_y[-1]):
np.append(pol_x, pol_x[0])
np.append(pol_y, pol_y[0])
# setting value for all grid
for x_ind in range(self.width):
for y_ind in range(self.height):
x_pos, y_pos = self.calc_grid_central_xy_position_from_xy_index(
x_ind, y_ind)
flag = self.check_inside_polygon(x_pos, y_pos, pol_x, pol_y)
if flag is inside:
self.set_value_from_xy_index(x_ind, y_ind, val)
| 463 |
|
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.calc_grid_index_from_xy_index
|
(self, x_ind, y_ind)
|
return grid_ind
| 137 | 139 |
def calc_grid_index_from_xy_index(self, x_ind, y_ind):
grid_ind = int(y_ind * self.width + x_ind)
return grid_ind
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L137-L139
| 2 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 77.037037 | 3 | 1 | 100 | 0 |
def calc_grid_index_from_xy_index(self, x_ind, y_ind):
grid_ind = int(y_ind * self.width + x_ind)
return grid_ind
| 464 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.calc_grid_central_xy_position_from_xy_index
|
(self, x_ind, y_ind)
|
return x_pos, y_pos
| 141 | 147 |
def calc_grid_central_xy_position_from_xy_index(self, x_ind, y_ind):
x_pos = self.calc_grid_central_xy_position_from_index(
x_ind, self.left_lower_x)
y_pos = self.calc_grid_central_xy_position_from_index(
y_ind, self.left_lower_y)
return x_pos, y_pos
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L141-L147
| 2 |
[
0,
1,
3,
5,
6
] | 71.428571 |
[] | 0 | false | 77.037037 | 7 | 1 | 100 | 0 |
def calc_grid_central_xy_position_from_xy_index(self, x_ind, y_ind):
x_pos = self.calc_grid_central_xy_position_from_index(
x_ind, self.left_lower_x)
y_pos = self.calc_grid_central_xy_position_from_index(
y_ind, self.left_lower_y)
return x_pos, y_pos
| 465 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.calc_grid_central_xy_position_from_index
|
(self, index, lower_pos)
|
return lower_pos + index * self.resolution + self.resolution / 2.0
| 149 | 150 |
def calc_grid_central_xy_position_from_index(self, index, lower_pos):
return lower_pos + index * self.resolution + self.resolution / 2.0
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L149-L150
| 2 |
[
0,
1
] | 100 |
[] | 0 | true | 77.037037 | 2 | 1 | 100 | 0 |
def calc_grid_central_xy_position_from_index(self, index, lower_pos):
return lower_pos + index * self.resolution + self.resolution / 2.0
| 466 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.calc_xy_index_from_position
|
(self, pos, lower_pos, max_index)
| 152 | 157 |
def calc_xy_index_from_position(self, pos, lower_pos, max_index):
ind = int(np.floor((pos - lower_pos) / self.resolution))
if 0 <= ind <= max_index:
return ind
else:
return None
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L152-L157
| 2 |
[
0,
1,
2,
3
] | 66.666667 |
[
5
] | 16.666667 | false | 77.037037 | 6 | 2 | 83.333333 | 0 |
def calc_xy_index_from_position(self, pos, lower_pos, max_index):
ind = int(np.floor((pos - lower_pos) / self.resolution))
if 0 <= ind <= max_index:
return ind
else:
return None
| 467 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.check_occupied_from_xy_index
|
(self, xind, yind, occupied_val=1.0)
| 159 | 166 |
def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
val = self.get_value_from_xy_index(xind, yind)
if val is None or val >= occupied_val:
return True
else:
return False
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L159-L166
| 2 |
[
0,
1,
2,
3,
4,
5,
7
] | 87.5 |
[] | 0 | false | 77.037037 | 8 | 3 | 100 | 0 |
def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
val = self.get_value_from_xy_index(xind, yind)
if val is None or val >= occupied_val:
return True
else:
return False
| 468 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.expand_grid
|
(self)
| 168 | 183 |
def expand_grid(self):
xinds, yinds = [], []
for ix in range(self.width):
for iy in range(self.height):
if self.check_occupied_from_xy_index(ix, iy):
xinds.append(ix)
yinds.append(iy)
for (ix, iy) in zip(xinds, yinds):
self.set_value_from_xy_index(ix + 1, iy, val=1.0)
self.set_value_from_xy_index(ix, iy + 1, val=1.0)
self.set_value_from_xy_index(ix + 1, iy + 1, val=1.0)
self.set_value_from_xy_index(ix - 1, iy, val=1.0)
self.set_value_from_xy_index(ix, iy - 1, val=1.0)
self.set_value_from_xy_index(ix - 1, iy - 1, val=1.0)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L168-L183
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15
] | 100 |
[] | 0 | true | 77.037037 | 16 | 5 | 100 | 0 |
def expand_grid(self):
xinds, yinds = [], []
for ix in range(self.width):
for iy in range(self.height):
if self.check_occupied_from_xy_index(ix, iy):
xinds.append(ix)
yinds.append(iy)
for (ix, iy) in zip(xinds, yinds):
self.set_value_from_xy_index(ix + 1, iy, val=1.0)
self.set_value_from_xy_index(ix, iy + 1, val=1.0)
self.set_value_from_xy_index(ix + 1, iy + 1, val=1.0)
self.set_value_from_xy_index(ix - 1, iy, val=1.0)
self.set_value_from_xy_index(ix, iy - 1, val=1.0)
self.set_value_from_xy_index(ix - 1, iy - 1, val=1.0)
| 469 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.check_inside_polygon
|
(iox, ioy, x, y)
|
return inside
| 186 | 204 |
def check_inside_polygon(iox, ioy, x, y):
npoint = len(x) - 1
inside = False
for i1 in range(npoint):
i2 = (i1 + 1) % (npoint + 1)
if x[i1] >= x[i2]:
min_x, max_x = x[i2], x[i1]
else:
min_x, max_x = x[i1], x[i2]
if not min_x <= iox < max_x:
continue
tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1])
if (y[i1] + tmp1 * (iox - x[i1]) - ioy) > 0.0:
inside = not inside
return inside
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L186-L204
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
10,
11,
12,
13,
14,
15,
16,
17,
18
] | 94.736842 |
[] | 0 | false | 77.037037 | 19 | 5 | 100 | 0 |
def check_inside_polygon(iox, ioy, x, y):
npoint = len(x) - 1
inside = False
for i1 in range(npoint):
i2 = (i1 + 1) % (npoint + 1)
if x[i1] >= x[i2]:
min_x, max_x = x[i2], x[i1]
else:
min_x, max_x = x[i1], x[i2]
if not min_x <= iox < max_x:
continue
tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1])
if (y[i1] + tmp1 * (iox - x[i1]) - ioy) > 0.0:
inside = not inside
return inside
| 470 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.print_grid_map_info
|
(self)
| 206 | 214 |
def print_grid_map_info(self):
print("width:", self.width)
print("height:", self.height)
print("resolution:", self.resolution)
print("center_x:", self.center_x)
print("center_y:", self.center_y)
print("left_lower_x:", self.left_lower_x)
print("left_lower_y:", self.left_lower_y)
print("ndata:", self.ndata)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L206-L214
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 77.037037 | 9 | 1 | 100 | 0 |
def print_grid_map_info(self):
print("width:", self.width)
print("height:", self.height)
print("resolution:", self.resolution)
print("center_x:", self.center_x)
print("center_y:", self.center_y)
print("left_lower_x:", self.left_lower_x)
print("left_lower_y:", self.left_lower_y)
print("ndata:", self.ndata)
| 471 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/grid_map_lib/grid_map_lib.py
|
GridMap.plot_grid_map
|
(self, ax=None)
|
return heat_map
| 216 | 225 |
def plot_grid_map(self, ax=None):
grid_data = np.reshape(np.array(self.data), (self.height, self.width))
if not ax:
fig, ax = plt.subplots()
heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0)
plt.axis("equal")
# plt.show()
return heat_map
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/grid_map_lib/grid_map_lib.py#L216-L225
| 2 |
[
0,
1
] | 20 |
[
2,
3,
4,
5,
6,
9
] | 60 | false | 77.037037 | 10 | 2 | 40 | 0 |
def plot_grid_map(self, ax=None):
grid_data = np.reshape(np.array(self.data), (self.height, self.width))
if not ax:
fig, ax = plt.subplots()
heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0)
plt.axis("equal")
# plt.show()
return heat_map
| 472 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/point_cloud_sampling/point_cloud_sampling.py
|
voxel_point_sampling
|
(original_points: npt.NDArray, voxel_size: float)
|
return points
|
Voxel Point Sampling function.
This function sample N-dimensional points with voxel grid.
Points in a same voxel grid will be merged by mean operation for sampling.
Parameters
----------
original_points : (M, N) N-dimensional points for sampling.
The number of points is M.
voxel_size : voxel grid size
Returns
-------
sampled points (M', N)
|
Voxel Point Sampling function.
This function sample N-dimensional points with voxel grid.
Points in a same voxel grid will be merged by mean operation for sampling.
| 16 | 38 |
def voxel_point_sampling(original_points: npt.NDArray, voxel_size: float):
"""
Voxel Point Sampling function.
This function sample N-dimensional points with voxel grid.
Points in a same voxel grid will be merged by mean operation for sampling.
Parameters
----------
original_points : (M, N) N-dimensional points for sampling.
The number of points is M.
voxel_size : voxel grid size
Returns
-------
sampled points (M', N)
"""
voxel_dict = defaultdict(list)
for i in range(original_points.shape[0]):
xyz = original_points[i, :]
xyz_index = tuple(xyz // voxel_size)
voxel_dict[xyz_index].append(xyz)
points = np.vstack([np.mean(v, axis=0) for v in voxel_dict.values()])
return points
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/point_cloud_sampling/point_cloud_sampling.py#L16-L38
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
] | 100 |
[] | 0 | true | 83.950617 | 23 | 3 | 100 | 13 |
def voxel_point_sampling(original_points: npt.NDArray, voxel_size: float):
voxel_dict = defaultdict(list)
for i in range(original_points.shape[0]):
xyz = original_points[i, :]
xyz_index = tuple(xyz // voxel_size)
voxel_dict[xyz_index].append(xyz)
points = np.vstack([np.mean(v, axis=0) for v in voxel_dict.values()])
return points
| 481 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/point_cloud_sampling/point_cloud_sampling.py
|
farthest_point_sampling
|
(orig_points: npt.NDArray,
n_points: int, seed: int)
|
return orig_points[selected_ids, :]
|
Farthest point sampling function
This function sample N-dimensional points with the farthest point policy.
Parameters
----------
orig_points : (M, N) N-dimensional points for sampling.
The number of points is M.
n_points : number of points for sampling
seed : random seed number
Returns
-------
sampled points (n_points, N)
|
Farthest point sampling function
This function sample N-dimensional points with the farthest point policy.
| 41 | 74 |
def farthest_point_sampling(orig_points: npt.NDArray,
n_points: int, seed: int):
"""
Farthest point sampling function
This function sample N-dimensional points with the farthest point policy.
Parameters
----------
orig_points : (M, N) N-dimensional points for sampling.
The number of points is M.
n_points : number of points for sampling
seed : random seed number
Returns
-------
sampled points (n_points, N)
"""
rng = np.random.default_rng(seed)
n_orig_points = orig_points.shape[0]
first_point_id = rng.choice(range(n_orig_points))
min_distances = np.ones(n_orig_points) * float("inf")
selected_ids = [first_point_id]
while len(selected_ids) < n_points:
base_point = orig_points[selected_ids[-1], :]
distances = np.linalg.norm(orig_points[np.newaxis, :] - base_point,
axis=2).flatten()
min_distances = np.minimum(min_distances, distances)
distances_rank = np.argsort(-min_distances) # Farthest order
for i in distances_rank: # From the farthest point
if i not in selected_ids: # if not selected yes, select it
selected_ids.append(i)
break
return orig_points[selected_ids, :]
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/point_cloud_sampling/point_cloud_sampling.py#L41-L74
| 2 |
[
0,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33
] | 52.941176 |
[] | 0 | false | 83.950617 | 34 | 4 | 100 | 13 |
def farthest_point_sampling(orig_points: npt.NDArray,
n_points: int, seed: int):
rng = np.random.default_rng(seed)
n_orig_points = orig_points.shape[0]
first_point_id = rng.choice(range(n_orig_points))
min_distances = np.ones(n_orig_points) * float("inf")
selected_ids = [first_point_id]
while len(selected_ids) < n_points:
base_point = orig_points[selected_ids[-1], :]
distances = np.linalg.norm(orig_points[np.newaxis, :] - base_point,
axis=2).flatten()
min_distances = np.minimum(min_distances, distances)
distances_rank = np.argsort(-min_distances) # Farthest order
for i in distances_rank: # From the farthest point
if i not in selected_ids: # if not selected yes, select it
selected_ids.append(i)
break
return orig_points[selected_ids, :]
| 482 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/point_cloud_sampling/point_cloud_sampling.py
|
poisson_disk_sampling
|
(orig_points: npt.NDArray, n_points: int,
min_distance: float, seed: int, MAX_ITER=1000)
|
return orig_points[selected_ids, :]
|
Poisson disk sampling function
This function sample N-dimensional points randomly until the number of
points keeping minimum distance between selected points.
Parameters
----------
orig_points : (M, N) N-dimensional points for sampling.
The number of points is M.
n_points : number of points for sampling
min_distance : minimum distance between selected points.
seed : random seed number
MAX_ITER : Maximum number of iteration. Default is 1000.
Returns
-------
sampled points (n_points or less, N)
|
Poisson disk sampling function
This function sample N-dimensional points randomly until the number of
points keeping minimum distance between selected points.
| 77 | 113 |
def poisson_disk_sampling(orig_points: npt.NDArray, n_points: int,
min_distance: float, seed: int, MAX_ITER=1000):
"""
Poisson disk sampling function
This function sample N-dimensional points randomly until the number of
points keeping minimum distance between selected points.
Parameters
----------
orig_points : (M, N) N-dimensional points for sampling.
The number of points is M.
n_points : number of points for sampling
min_distance : minimum distance between selected points.
seed : random seed number
MAX_ITER : Maximum number of iteration. Default is 1000.
Returns
-------
sampled points (n_points or less, N)
"""
rng = np.random.default_rng(seed)
selected_id = rng.choice(range(orig_points.shape[0]))
selected_ids = [selected_id]
loop = 0
while len(selected_ids) < n_points and loop <= MAX_ITER:
selected_id = rng.choice(range(orig_points.shape[0]))
base_point = orig_points[selected_id, :]
distances = np.linalg.norm(
orig_points[np.newaxis, selected_ids] - base_point,
axis=2).flatten()
if min(distances) >= min_distance:
selected_ids.append(selected_id)
loop += 1
if len(selected_ids) != n_points:
print("Could not find the specified number of points...")
return orig_points[selected_ids, :]
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/point_cloud_sampling/point_cloud_sampling.py#L77-L113
| 2 |
[
0,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
35,
36
] | 48.648649 |
[
34
] | 2.702703 | false | 83.950617 | 37 | 5 | 97.297297 | 16 |
def poisson_disk_sampling(orig_points: npt.NDArray, n_points: int,
min_distance: float, seed: int, MAX_ITER=1000):
rng = np.random.default_rng(seed)
selected_id = rng.choice(range(orig_points.shape[0]))
selected_ids = [selected_id]
loop = 0
while len(selected_ids) < n_points and loop <= MAX_ITER:
selected_id = rng.choice(range(orig_points.shape[0]))
base_point = orig_points[selected_id, :]
distances = np.linalg.norm(
orig_points[np.newaxis, selected_ids] - base_point,
axis=2).flatten()
if min(distances) >= min_distance:
selected_ids.append(selected_id)
loop += 1
if len(selected_ids) != n_points:
print("Could not find the specified number of points...")
return orig_points[selected_ids, :]
| 483 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/point_cloud_sampling/point_cloud_sampling.py
|
plot_sampled_points
|
(original_points, sampled_points, method_name)
| 116 | 126 |
def plot_sampled_points(original_points, sampled_points, method_name):
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(original_points[:, 0], original_points[:, 1],
original_points[:, 2], marker=".", label="Original points")
ax.scatter(sampled_points[:, 0], sampled_points[:, 1],
sampled_points[:, 2], marker="o",
label="Filtered points")
plt.legend()
plt.title(method_name)
plt.axis("equal")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/point_cloud_sampling/point_cloud_sampling.py#L116-L126
| 2 |
[
0
] | 9.090909 |
[
1,
2,
3,
5,
8,
9,
10
] | 63.636364 | false | 83.950617 | 11 | 1 | 36.363636 | 0 |
def plot_sampled_points(original_points, sampled_points, method_name):
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(original_points[:, 0], original_points[:, 1],
original_points[:, 2], marker=".", label="Original points")
ax.scatter(sampled_points[:, 0], sampled_points[:, 1],
sampled_points[:, 2], marker="o",
label="Filtered points")
plt.legend()
plt.title(method_name)
plt.axis("equal")
| 484 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/gaussian_grid_map/gaussian_grid_map.py
|
generate_gaussian_grid_map
|
(ox, oy, xyreso, std)
|
return gmap, minx, maxx, miny, maxy
| 19 | 41 |
def generate_gaussian_grid_map(ox, oy, xyreso, std):
minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
gmap = [[0.0 for i in range(yw)] for i in range(xw)]
for ix in range(xw):
for iy in range(yw):
x = ix * xyreso + minx
y = iy * xyreso + miny
# Search minimum distance
mindis = float("inf")
for (iox, ioy) in zip(ox, oy):
d = math.hypot(iox - x, ioy - y)
if mindis >= d:
mindis = d
pdf = (1.0 - norm.cdf(mindis, 0.0, std))
gmap[ix][iy] = pdf
return gmap, minx, maxx, miny, maxy
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/gaussian_grid_map/gaussian_grid_map.py#L19-L41
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
] | 100 |
[] | 0 | true | 90.697674 | 23 | 7 | 100 | 0 |
def generate_gaussian_grid_map(ox, oy, xyreso, std):
minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
gmap = [[0.0 for i in range(yw)] for i in range(xw)]
for ix in range(xw):
for iy in range(yw):
x = ix * xyreso + minx
y = iy * xyreso + miny
# Search minimum distance
mindis = float("inf")
for (iox, ioy) in zip(ox, oy):
d = math.hypot(iox - x, ioy - y)
if mindis >= d:
mindis = d
pdf = (1.0 - norm.cdf(mindis, 0.0, std))
gmap[ix][iy] = pdf
return gmap, minx, maxx, miny, maxy
| 485 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/gaussian_grid_map/gaussian_grid_map.py
|
calc_grid_map_config
|
(ox, oy, xyreso)
|
return minx, miny, maxx, maxy, xw, yw
| 44 | 52 |
def calc_grid_map_config(ox, oy, xyreso):
minx = round(min(ox) - EXTEND_AREA / 2.0)
miny = round(min(oy) - EXTEND_AREA / 2.0)
maxx = round(max(ox) + EXTEND_AREA / 2.0)
maxy = round(max(oy) + EXTEND_AREA / 2.0)
xw = int(round((maxx - minx) / xyreso))
yw = int(round((maxy - miny) / xyreso))
return minx, miny, maxx, maxy, xw, yw
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/gaussian_grid_map/gaussian_grid_map.py#L44-L52
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 90.697674 | 9 | 1 | 100 | 0 |
def calc_grid_map_config(ox, oy, xyreso):
minx = round(min(ox) - EXTEND_AREA / 2.0)
miny = round(min(oy) - EXTEND_AREA / 2.0)
maxx = round(max(ox) + EXTEND_AREA / 2.0)
maxy = round(max(oy) + EXTEND_AREA / 2.0)
xw = int(round((maxx - minx) / xyreso))
yw = int(round((maxy - miny) / xyreso))
return minx, miny, maxx, maxy, xw, yw
| 486 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/gaussian_grid_map/gaussian_grid_map.py
|
draw_heatmap
|
(data, minx, maxx, miny, maxy, xyreso)
| 55 | 59 |
def draw_heatmap(data, minx, maxx, miny, maxy, xyreso):
x, y = np.mgrid[slice(minx - xyreso / 2.0, maxx + xyreso / 2.0, xyreso),
slice(miny - xyreso / 2.0, maxy + xyreso / 2.0, xyreso)]
plt.pcolor(x, y, data, vmax=1.0, cmap=plt.cm.Blues)
plt.axis("equal")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/gaussian_grid_map/gaussian_grid_map.py#L55-L59
| 2 |
[
0
] | 20 |
[
1,
3,
4
] | 60 | false | 90.697674 | 5 | 1 | 40 | 0 |
def draw_heatmap(data, minx, maxx, miny, maxy, xyreso):
x, y = np.mgrid[slice(minx - xyreso / 2.0, maxx + xyreso / 2.0, xyreso),
slice(miny - xyreso / 2.0, maxy + xyreso / 2.0, xyreso)]
plt.pcolor(x, y, data, vmax=1.0, cmap=plt.cm.Blues)
plt.axis("equal")
| 487 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/gaussian_grid_map/gaussian_grid_map.py
|
main
|
()
| 62 | 82 |
def main():
print(__file__ + " start!!")
xyreso = 0.5 # xy grid resolution
STD = 5.0 # standard diviation for gaussian distribution
for i in range(5):
ox = (np.random.rand(4) - 0.5) * 10.0
oy = (np.random.rand(4) - 0.5) * 10.0
gmap, minx, maxx, miny, maxy = generate_gaussian_grid_map(
ox, oy, xyreso, STD)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso)
plt.plot(ox, oy, "xr")
plt.plot(0.0, 0.0, "ob")
plt.pause(1.0)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/gaussian_grid_map/gaussian_grid_map.py#L62-L82
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
] | 71.428571 |
[] | 0 | false | 90.697674 | 21 | 3 | 100 | 0 |
def main():
print(__file__ + " start!!")
xyreso = 0.5 # xy grid resolution
STD = 5.0 # standard diviation for gaussian distribution
for i in range(5):
ox = (np.random.rand(4) - 0.5) * 10.0
oy = (np.random.rand(4) - 0.5) * 10.0
gmap, minx, maxx, miny, maxy = generate_gaussian_grid_map(
ox, oy, xyreso, STD)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso)
plt.plot(ox, oy, "xr")
plt.plot(0.0, 0.0, "ob")
plt.pause(1.0)
| 488 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
main
|
()
| 211 | 265 |
def main():
# simulation parameters
sim_time = 30.0 # simulation time
dt = 0.2 # time tick
angle_resolution = np.deg2rad(3.0) # sensor angle resolution
v1 = VehicleSimulator(-10.0, 0.0, np.deg2rad(90.0),
0.0, 50.0 / 3.6, 3.0, 5.0)
v2 = VehicleSimulator(20.0, 10.0, np.deg2rad(180.0),
0.0, 50.0 / 3.6, 4.0, 10.0)
l_shape_fitting = LShapeFitting()
lidar_sim = LidarSimulator()
time = 0.0
while time <= sim_time:
time += dt
v1.update(dt, 0.1, 0.0)
v2.update(dt, 0.1, -0.05)
ox, oy = lidar_sim.get_observation_points([v1, v2], angle_resolution)
rects, id_sets = l_shape_fitting.fitting(ox, oy)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.axis("equal")
plt.plot(0.0, 0.0, "*r")
v1.plot()
v2.plot()
# Plot range observation
for ids in id_sets:
x = [ox[i] for i in range(len(ox)) if i in ids]
y = [oy[i] for i in range(len(ox)) if i in ids]
for (ix, iy) in zip(x, y):
plt.plot([0.0, ix], [0.0, iy], "-og")
plt.plot([ox[i] for i in range(len(ox)) if i in ids],
[oy[i] for i in range(len(ox)) if i in ids],
"o")
for rect in rects:
rect.plot()
plt.pause(0.1)
print("Done")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L211-L265
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
10,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
53,
54
] | 69.230769 |
[] | 0 | false | 81.935484 | 55 | 10 | 100 | 0 |
def main():
# simulation parameters
sim_time = 30.0 # simulation time
dt = 0.2 # time tick
angle_resolution = np.deg2rad(3.0) # sensor angle resolution
v1 = VehicleSimulator(-10.0, 0.0, np.deg2rad(90.0),
0.0, 50.0 / 3.6, 3.0, 5.0)
v2 = VehicleSimulator(20.0, 10.0, np.deg2rad(180.0),
0.0, 50.0 / 3.6, 4.0, 10.0)
l_shape_fitting = LShapeFitting()
lidar_sim = LidarSimulator()
time = 0.0
while time <= sim_time:
time += dt
v1.update(dt, 0.1, 0.0)
v2.update(dt, 0.1, -0.05)
ox, oy = lidar_sim.get_observation_points([v1, v2], angle_resolution)
rects, id_sets = l_shape_fitting.fitting(ox, oy)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.axis("equal")
plt.plot(0.0, 0.0, "*r")
v1.plot()
v2.plot()
# Plot range observation
for ids in id_sets:
x = [ox[i] for i in range(len(ox)) if i in ids]
y = [oy[i] for i in range(len(ox)) if i in ids]
for (ix, iy) in zip(x, y):
plt.plot([0.0, ix], [0.0, iy], "-og")
plt.plot([ox[i] for i in range(len(ox)) if i in ids],
[oy[i] for i in range(len(ox)) if i in ids],
"o")
for rect in rects:
rect.plot()
plt.pause(0.1)
print("Done")
| 489 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting.__init__
|
(self)
| 39 | 45 |
def __init__(self):
# Parameters
self.criteria = self.Criteria.VARIANCE
self.min_dist_of_closeness_criteria = 0.01 # [m]
self.d_theta_deg_for_search = 1.0 # [deg]
self.R0 = 3.0 # [m] range segmentation param
self.Rd = 0.001
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L39-L45
| 2 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 81.935484 | 7 | 1 | 100 | 0 |
def __init__(self):
# Parameters
self.criteria = self.Criteria.VARIANCE
self.min_dist_of_closeness_criteria = 0.01 # [m]
self.d_theta_deg_for_search = 1.0 # [deg]
self.R0 = 3.0 # [m] range segmentation param
self.Rd = 0.001
| 490 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting.fitting
|
(self, ox, oy)
|
return rects, id_sets
| 47 | 59 |
def fitting(self, ox, oy):
# step1: Adaptive Range Segmentation
id_sets = self._adoptive_range_segmentation(ox, oy)
# step2 Rectangle search
rects = []
for ids in id_sets: # for each cluster
cx = [ox[i] for i in range(len(ox)) if i in ids]
cy = [oy[i] for i in range(len(oy)) if i in ids]
rects.append(self._rectangle_search(cx, cy))
return rects, id_sets
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L47-L59
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 81.935484 | 13 | 4 | 100 | 0 |
def fitting(self, ox, oy):
# step1: Adaptive Range Segmentation
id_sets = self._adoptive_range_segmentation(ox, oy)
# step2 Rectangle search
rects = []
for ids in id_sets: # for each cluster
cx = [ox[i] for i in range(len(ox)) if i in ids]
cy = [oy[i] for i in range(len(oy)) if i in ids]
rects.append(self._rectangle_search(cx, cy))
return rects, id_sets
| 491 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting._calc_area_criterion
|
(c1, c2)
|
return alpha
| 62 | 70 |
def _calc_area_criterion(c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
alpha = -(c1_max - c1_min) * (c2_max - c2_min)
return alpha
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L62-L70
| 2 |
[
0
] | 11.111111 |
[
1,
2,
3,
4,
6,
8
] | 66.666667 | false | 81.935484 | 9 | 1 | 33.333333 | 0 |
def _calc_area_criterion(c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
alpha = -(c1_max - c1_min) * (c2_max - c2_min)
return alpha
| 492 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting._calc_closeness_criterion
|
(self, c1, c2)
|
return beta
| 72 | 84 |
def _calc_closeness_criterion(self, c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
# Vectorization
D1 = np.minimum(c1_max - c1, c1 - c1_min)
D2 = np.minimum(c2_max - c2, c2 - c2_min)
d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria)
beta = (1.0 / d).sum()
return beta
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L72-L84
| 2 |
[
0
] | 7.692308 |
[
1,
2,
3,
4,
7,
8,
9,
10,
12
] | 69.230769 | false | 81.935484 | 13 | 1 | 30.769231 | 0 |
def _calc_closeness_criterion(self, c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
# Vectorization
D1 = np.minimum(c1_max - c1, c1 - c1_min)
D2 = np.minimum(c2_max - c2, c2 - c2_min)
d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria)
beta = (1.0 / d).sum()
return beta
| 493 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting._calc_variance_criterion
|
(c1, c2)
|
return gamma
| 87 | 102 |
def _calc_variance_criterion(c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
# Vectorization
D1 = np.minimum(c1_max - c1, c1 - c1_min)
D2 = np.minimum(c2_max - c2, c2 - c2_min)
E1 = D1[D1 < D2]
E2 = D2[D1 >= D2]
V1 = - np.var(E1) if len(E1) > 0 else 0.
V2 = - np.var(E2) if len(E2) > 0 else 0.
gamma = V1 + V2
return gamma
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L87-L102
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15
] | 100 |
[] | 0 | true | 81.935484 | 16 | 1 | 100 | 0 |
def _calc_variance_criterion(c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
# Vectorization
D1 = np.minimum(c1_max - c1, c1 - c1_min)
D2 = np.minimum(c2_max - c2, c2 - c2_min)
E1 = D1[D1 < D2]
E2 = D2[D1 >= D2]
V1 = - np.var(E1) if len(E1) > 0 else 0.
V2 = - np.var(E2) if len(E2) > 0 else 0.
gamma = V1 + V2
return gamma
| 494 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting._rectangle_search
|
(self, x, y)
|
return rect
| 104 | 149 |
def _rectangle_search(self, x, y):
X = np.array([x, y]).T
d_theta = np.deg2rad(self.d_theta_deg_for_search)
min_cost = (-float('inf'), None)
for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
c = X @ rot_mat_2d(theta)
c1 = c[:, 0]
c2 = c[:, 1]
# Select criteria
cost = 0.0
if self.criteria == self.Criteria.AREA:
cost = self._calc_area_criterion(c1, c2)
elif self.criteria == self.Criteria.CLOSENESS:
cost = self._calc_closeness_criterion(c1, c2)
elif self.criteria == self.Criteria.VARIANCE:
cost = self._calc_variance_criterion(c1, c2)
if min_cost[0] < cost:
min_cost = (cost, theta)
# calc best rectangle
sin_s = np.sin(min_cost[1])
cos_s = np.cos(min_cost[1])
c1_s = X @ np.array([cos_s, sin_s]).T
c2_s = X @ np.array([-sin_s, cos_s]).T
rect = RectangleData()
rect.a[0] = cos_s
rect.b[0] = sin_s
rect.c[0] = min(c1_s)
rect.a[1] = -sin_s
rect.b[1] = cos_s
rect.c[1] = min(c2_s)
rect.a[2] = cos_s
rect.b[2] = sin_s
rect.c[2] = max(c1_s)
rect.a[3] = -sin_s
rect.b[3] = cos_s
rect.c[3] = max(c2_s)
return rect
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L104-L149
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
16,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45
] | 95.652174 |
[
15,
17
] | 4.347826 | false | 81.935484 | 46 | 6 | 95.652174 | 0 |
def _rectangle_search(self, x, y):
X = np.array([x, y]).T
d_theta = np.deg2rad(self.d_theta_deg_for_search)
min_cost = (-float('inf'), None)
for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
c = X @ rot_mat_2d(theta)
c1 = c[:, 0]
c2 = c[:, 1]
# Select criteria
cost = 0.0
if self.criteria == self.Criteria.AREA:
cost = self._calc_area_criterion(c1, c2)
elif self.criteria == self.Criteria.CLOSENESS:
cost = self._calc_closeness_criterion(c1, c2)
elif self.criteria == self.Criteria.VARIANCE:
cost = self._calc_variance_criterion(c1, c2)
if min_cost[0] < cost:
min_cost = (cost, theta)
# calc best rectangle
sin_s = np.sin(min_cost[1])
cos_s = np.cos(min_cost[1])
c1_s = X @ np.array([cos_s, sin_s]).T
c2_s = X @ np.array([-sin_s, cos_s]).T
rect = RectangleData()
rect.a[0] = cos_s
rect.b[0] = sin_s
rect.c[0] = min(c1_s)
rect.a[1] = -sin_s
rect.b[1] = cos_s
rect.c[1] = min(c2_s)
rect.a[2] = cos_s
rect.b[2] = sin_s
rect.c[2] = max(c1_s)
rect.a[3] = -sin_s
rect.b[3] = cos_s
rect.c[3] = max(c2_s)
return rect
| 495 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
LShapeFitting._adoptive_range_segmentation
|
(self, ox, oy)
|
return S
| 151 | 175 |
def _adoptive_range_segmentation(self, ox, oy):
# Setup initial cluster
S = []
for i, _ in enumerate(ox):
C = set()
R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
for j, _ in enumerate(ox):
d = np.hypot(ox[i] - ox[j], oy[i] - oy[j])
if d <= R:
C.add(j)
S.append(C)
# Merge cluster
while True:
no_change = True
for (c1, c2) in list(itertools.permutations(range(len(S)), 2)):
if S[c1] & S[c2]:
S[c1] = (S[c1] | S.pop(c2))
no_change = False
break
if no_change:
break
return S
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L151-L175
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24
] | 100 |
[] | 0 | true | 81.935484 | 25 | 8 | 100 | 0 |
def _adoptive_range_segmentation(self, ox, oy):
# Setup initial cluster
S = []
for i, _ in enumerate(ox):
C = set()
R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
for j, _ in enumerate(ox):
d = np.hypot(ox[i] - ox[j], oy[i] - oy[j])
if d <= R:
C.add(j)
S.append(C)
# Merge cluster
while True:
no_change = True
for (c1, c2) in list(itertools.permutations(range(len(S)), 2)):
if S[c1] & S[c2]:
S[c1] = (S[c1] | S.pop(c2))
no_change = False
break
if no_change:
break
return S
| 496 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
RectangleData.__init__
|
(self)
| 180 | 186 |
def __init__(self):
self.a = [None] * 4
self.b = [None] * 4
self.c = [None] * 4
self.rect_c_x = [None] * 5
self.rect_c_y = [None] * 5
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L180-L186
| 2 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 81.935484 | 7 | 1 | 100 | 0 |
def __init__(self):
self.a = [None] * 4
self.b = [None] * 4
self.c = [None] * 4
self.rect_c_x = [None] * 5
self.rect_c_y = [None] * 5
| 497 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
RectangleData.plot
|
(self)
| 188 | 190 |
def plot(self):
self.calc_rect_contour()
plt.plot(self.rect_c_x, self.rect_c_y, "-r")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L188-L190
| 2 |
[
0
] | 33.333333 |
[
1,
2
] | 66.666667 | false | 81.935484 | 3 | 1 | 33.333333 | 0 |
def plot(self):
self.calc_rect_contour()
plt.plot(self.rect_c_x, self.rect_c_y, "-r")
| 498 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
RectangleData.calc_rect_contour
|
(self)
| 192 | 202 |
def calc_rect_contour(self):
self.rect_c_x[0], self.rect_c_y[0] = self.calc_cross_point(
self.a[0:2], self.b[0:2], self.c[0:2])
self.rect_c_x[1], self.rect_c_y[1] = self.calc_cross_point(
self.a[1:3], self.b[1:3], self.c[1:3])
self.rect_c_x[2], self.rect_c_y[2] = self.calc_cross_point(
self.a[2:4], self.b[2:4], self.c[2:4])
self.rect_c_x[3], self.rect_c_y[3] = self.calc_cross_point(
[self.a[3], self.a[0]], [self.b[3], self.b[0]], [self.c[3], self.c[0]])
self.rect_c_x[4], self.rect_c_y[4] = self.rect_c_x[0], self.rect_c_y[0]
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L192-L202
| 2 |
[
0,
1
] | 18.181818 |
[
2,
4,
6,
8,
10
] | 45.454545 | false | 81.935484 | 11 | 1 | 54.545455 | 0 |
def calc_rect_contour(self):
self.rect_c_x[0], self.rect_c_y[0] = self.calc_cross_point(
self.a[0:2], self.b[0:2], self.c[0:2])
self.rect_c_x[1], self.rect_c_y[1] = self.calc_cross_point(
self.a[1:3], self.b[1:3], self.c[1:3])
self.rect_c_x[2], self.rect_c_y[2] = self.calc_cross_point(
self.a[2:4], self.b[2:4], self.c[2:4])
self.rect_c_x[3], self.rect_c_y[3] = self.calc_cross_point(
[self.a[3], self.a[0]], [self.b[3], self.b[0]], [self.c[3], self.c[0]])
self.rect_c_x[4], self.rect_c_y[4] = self.rect_c_x[0], self.rect_c_y[0]
| 499 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/rectangle_fitting.py
|
RectangleData.calc_cross_point
|
(a, b, c)
|
return x, y
| 205 | 208 |
def calc_cross_point(a, b, c):
x = (b[0] * -c[1] - b[1] * -c[0]) / (a[0] * b[1] - a[1] * b[0])
y = (a[1] * -c[0] - a[0] * -c[1]) / (a[0] * b[1] - a[1] * b[0])
return x, y
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/rectangle_fitting.py#L205-L208
| 2 |
[
0
] | 25 |
[
1,
2,
3
] | 75 | false | 81.935484 | 4 | 1 | 25 | 0 |
def calc_cross_point(a, b, c):
x = (b[0] * -c[1] - b[1] * -c[0]) / (a[0] * b[1] - a[1] * b[0])
y = (a[1] * -c[0] - a[0] * -c[1]) / (a[0] * b[1] - a[1] * b[0])
return x, y
| 500 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator.__init__
|
(self, i_x, i_y, i_yaw, i_v, max_v, w, L)
| 22 | 30 |
def __init__(self, i_x, i_y, i_yaw, i_v, max_v, w, L):
self.x = i_x
self.y = i_y
self.yaw = i_yaw
self.v = i_v
self.max_v = max_v
self.W = w
self.L = L
self._calc_vehicle_contour()
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L22-L30
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 95.454545 | 9 | 1 | 100 | 0 |
def __init__(self, i_x, i_y, i_yaw, i_v, max_v, w, L):
self.x = i_x
self.y = i_y
self.yaw = i_yaw
self.v = i_v
self.max_v = max_v
self.W = w
self.L = L
self._calc_vehicle_contour()
| 501 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator.update
|
(self, dt, a, omega)
| 32 | 38 |
def update(self, dt, a, omega):
self.x += self.v * np.cos(self.yaw) * dt
self.y += self.v * np.sin(self.yaw) * dt
self.yaw += omega * dt
self.v += a * dt
if self.v >= self.max_v:
self.v = self.max_v
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L32-L38
| 2 |
[
0,
1,
2,
3,
4,
5
] | 85.714286 |
[
6
] | 14.285714 | false | 95.454545 | 7 | 2 | 85.714286 | 0 |
def update(self, dt, a, omega):
self.x += self.v * np.cos(self.yaw) * dt
self.y += self.v * np.sin(self.yaw) * dt
self.yaw += omega * dt
self.v += a * dt
if self.v >= self.max_v:
self.v = self.max_v
| 502 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator.plot
|
(self)
| 40 | 45 |
def plot(self):
plt.plot(self.x, self.y, ".b")
# convert global coordinate
gx, gy = self.calc_global_contour()
plt.plot(gx, gy, "--b")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L40-L45
| 2 |
[
0
] | 16.666667 |
[
1,
4,
5
] | 50 | false | 95.454545 | 6 | 1 | 50 | 0 |
def plot(self):
plt.plot(self.x, self.y, ".b")
# convert global coordinate
gx, gy = self.calc_global_contour()
plt.plot(gx, gy, "--b")
| 503 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator.calc_global_contour
|
(self)
|
return gx, gy
| 47 | 52 |
def calc_global_contour(self):
gxy = np.stack([self.vc_x, self.vc_y]).T @ rot_mat_2d(self.yaw)
gx = gxy[:, 0] + self.x
gy = gxy[:, 1] + self.y
return gx, gy
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L47-L52
| 2 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 95.454545 | 6 | 1 | 100 | 0 |
def calc_global_contour(self):
gxy = np.stack([self.vc_x, self.vc_y]).T @ rot_mat_2d(self.yaw)
gx = gxy[:, 0] + self.x
gy = gxy[:, 1] + self.y
return gx, gy
| 504 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator._calc_vehicle_contour
|
(self)
| 54 | 74 |
def _calc_vehicle_contour(self):
self.vc_x = []
self.vc_y = []
self.vc_x.append(self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x.append(self.L / 2.0)
self.vc_y.append(-self.W / 2.0)
self.vc_x.append(-self.L / 2.0)
self.vc_y.append(-self.W / 2.0)
self.vc_x.append(-self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x.append(self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x, self.vc_y = self._interpolate(self.vc_x, self.vc_y)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L54-L74
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
] | 100 |
[] | 0 | true | 95.454545 | 21 | 1 | 100 | 0 |
def _calc_vehicle_contour(self):
self.vc_x = []
self.vc_y = []
self.vc_x.append(self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x.append(self.L / 2.0)
self.vc_y.append(-self.W / 2.0)
self.vc_x.append(-self.L / 2.0)
self.vc_y.append(-self.W / 2.0)
self.vc_x.append(-self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x.append(self.L / 2.0)
self.vc_y.append(self.W / 2.0)
self.vc_x, self.vc_y = self._interpolate(self.vc_x, self.vc_y)
| 505 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
VehicleSimulator._interpolate
|
(x, y)
|
return rx, ry
| 77 | 91 |
def _interpolate(x, y):
rx, ry = [], []
d_theta = 0.05
for i in range(len(x) - 1):
rx.extend([(1.0 - theta) * x[i] + theta * x[i + 1]
for theta in np.arange(0.0, 1.0, d_theta)])
ry.extend([(1.0 - theta) * y[i] + theta * y[i + 1]
for theta in np.arange(0.0, 1.0, d_theta)])
rx.extend([(1.0 - theta) * x[len(x) - 1] + theta * x[1]
for theta in np.arange(0.0, 1.0, d_theta)])
ry.extend([(1.0 - theta) * y[len(y) - 1] + theta * y[1]
for theta in np.arange(0.0, 1.0, d_theta)])
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L77-L91
| 2 |
[
0,
1,
2,
3,
4,
6,
8,
9,
11,
13,
14
] | 73.333333 |
[] | 0 | false | 95.454545 | 15 | 6 | 100 | 0 |
def _interpolate(x, y):
rx, ry = [], []
d_theta = 0.05
for i in range(len(x) - 1):
rx.extend([(1.0 - theta) * x[i] + theta * x[i + 1]
for theta in np.arange(0.0, 1.0, d_theta)])
ry.extend([(1.0 - theta) * y[i] + theta * y[i + 1]
for theta in np.arange(0.0, 1.0, d_theta)])
rx.extend([(1.0 - theta) * x[len(x) - 1] + theta * x[1]
for theta in np.arange(0.0, 1.0, d_theta)])
ry.extend([(1.0 - theta) * y[len(y) - 1] + theta * y[1]
for theta in np.arange(0.0, 1.0, d_theta)])
return rx, ry
| 506 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
LidarSimulator.__init__
|
(self)
| 96 | 97 |
def __init__(self):
self.range_noise = 0.01
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L96-L97
| 2 |
[
0,
1
] | 100 |
[] | 0 | true | 95.454545 | 2 | 1 | 100 | 0 |
def __init__(self):
self.range_noise = 0.01
| 507 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
LidarSimulator.get_observation_points
|
(self, v_list, angle_resolution)
|
return rx, ry
| 99 | 120 |
def get_observation_points(self, v_list, angle_resolution):
x, y, angle, r = [], [], [], []
# store all points
for v in v_list:
gx, gy = v.calc_global_contour()
for vx, vy in zip(gx, gy):
v_angle = math.atan2(vy, vx)
vr = np.hypot(vx, vy) * random.uniform(1.0 - self.range_noise,
1.0 + self.range_noise)
x.append(vx)
y.append(vy)
angle.append(v_angle)
r.append(vr)
# ray casting filter
rx, ry = self.ray_casting_filter(angle, r, angle_resolution)
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L99-L120
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21
] | 95.454545 |
[] | 0 | false | 95.454545 | 22 | 3 | 100 | 0 |
def get_observation_points(self, v_list, angle_resolution):
x, y, angle, r = [], [], [], []
# store all points
for v in v_list:
gx, gy = v.calc_global_contour()
for vx, vy in zip(gx, gy):
v_angle = math.atan2(vy, vx)
vr = np.hypot(vx, vy) * random.uniform(1.0 - self.range_noise,
1.0 + self.range_noise)
x.append(vx)
y.append(vy)
angle.append(v_angle)
r.append(vr)
# ray casting filter
rx, ry = self.ray_casting_filter(angle, r, angle_resolution)
return rx, ry
| 508 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/rectangle_fitting/simulator.py
|
LidarSimulator.ray_casting_filter
|
(theta_l, range_l, angle_resolution)
|
return rx, ry
| 123 | 140 |
def ray_casting_filter(theta_l, range_l, angle_resolution):
rx, ry = [], []
range_db = [float("inf") for _ in range(
int(np.floor((np.pi * 2.0) / angle_resolution)) + 1)]
for i in range(len(theta_l)):
angle_id = int(round(theta_l[i] / angle_resolution))
if range_db[angle_id] > range_l[i]:
range_db[angle_id] = range_l[i]
for i in range(len(range_db)):
t = i * angle_resolution
if range_db[i] != float("inf"):
rx.append(range_db[i] * np.cos(t))
ry.append(range_db[i] * np.sin(t))
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/rectangle_fitting/simulator.py#L123-L140
| 2 |
[
0,
1,
2,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
] | 94.444444 |
[] | 0 | false | 95.454545 | 18 | 6 | 100 | 0 |
def ray_casting_filter(theta_l, range_l, angle_resolution):
rx, ry = [], []
range_db = [float("inf") for _ in range(
int(np.floor((np.pi * 2.0) / angle_resolution)) + 1)]
for i in range(len(theta_l)):
angle_id = int(round(theta_l[i] / angle_resolution))
if range_db[angle_id] > range_l[i]:
range_db[angle_id] = range_l[i]
for i in range(len(range_db)):
t = i * angle_resolution
if range_db[i] != float("inf"):
rx.append(range_db[i] * np.cos(t))
ry.append(range_db[i] * np.sin(t))
return rx, ry
| 509 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/circle_fitting/circle_fitting.py
|
circle_fitting
|
(x, y)
|
return (cxe, cye, re, error)
|
Circle Fitting with least squared
input: point x-y positions
output cxe x center position
cye y center position
re radius of circle
error: prediction error
|
Circle Fitting with least squared
input: point x-y positions
output cxe x center position
cye y center position
re radius of circle
error: prediction error
| 17 | 49 |
def circle_fitting(x, y):
"""
Circle Fitting with least squared
input: point x-y positions
output cxe x center position
cye y center position
re radius of circle
error: prediction error
"""
sumx = sum(x)
sumy = sum(y)
sumx2 = sum([ix ** 2 for ix in x])
sumy2 = sum([iy ** 2 for iy in y])
sumxy = sum([ix * iy for (ix, iy) in zip(x, y)])
F = np.array([[sumx2, sumxy, sumx],
[sumxy, sumy2, sumy],
[sumx, sumy, len(x)]])
G = np.array([[-sum([ix ** 3 + ix * iy ** 2 for (ix, iy) in zip(x, y)])],
[-sum([ix ** 2 * iy + iy ** 3 for (ix, iy) in zip(x, y)])],
[-sum([ix ** 2 + iy ** 2 for (ix, iy) in zip(x, y)])]])
T = np.linalg.inv(F).dot(G)
cxe = float(T[0] / -2)
cye = float(T[1] / -2)
re = math.sqrt(cxe**2 + cye**2 - T[2])
error = sum([np.hypot(cxe - ix, cye - iy) - re for (ix, iy) in zip(x, y)])
return (cxe, cye, re, error)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/circle_fitting/circle_fitting.py#L17-L49
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32
] | 100 |
[] | 0 | true | 98.4375 | 33 | 8 | 100 | 6 |
def circle_fitting(x, y):
sumx = sum(x)
sumy = sum(y)
sumx2 = sum([ix ** 2 for ix in x])
sumy2 = sum([iy ** 2 for iy in y])
sumxy = sum([ix * iy for (ix, iy) in zip(x, y)])
F = np.array([[sumx2, sumxy, sumx],
[sumxy, sumy2, sumy],
[sumx, sumy, len(x)]])
G = np.array([[-sum([ix ** 3 + ix * iy ** 2 for (ix, iy) in zip(x, y)])],
[-sum([ix ** 2 * iy + iy ** 3 for (ix, iy) in zip(x, y)])],
[-sum([ix ** 2 + iy ** 2 for (ix, iy) in zip(x, y)])]])
T = np.linalg.inv(F).dot(G)
cxe = float(T[0] / -2)
cye = float(T[1] / -2)
re = math.sqrt(cxe**2 + cye**2 - T[2])
error = sum([np.hypot(cxe - ix, cye - iy) - re for (ix, iy) in zip(x, y)])
return (cxe, cye, re, error)
| 510 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/circle_fitting/circle_fitting.py
|
get_sample_points
|
(cx, cy, cr, angle_reso)
|
return rx, ry
| 52 | 70 |
def get_sample_points(cx, cy, cr, angle_reso):
x, y, angle, r = [], [], [], []
# points sampling
for theta in np.arange(0.0, 2.0 * math.pi, angle_reso):
nx = cx + cr * math.cos(theta)
ny = cy + cr * math.sin(theta)
nangle = math.atan2(ny, nx)
nr = math.hypot(nx, ny) * random.uniform(0.95, 1.05)
x.append(nx)
y.append(ny)
angle.append(nangle)
r.append(nr)
# ray casting filter
rx, ry = ray_casting_filter(x, y, angle, r, angle_reso)
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/circle_fitting/circle_fitting.py#L52-L70
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
] | 100 |
[] | 0 | true | 98.4375 | 19 | 2 | 100 | 0 |
def get_sample_points(cx, cy, cr, angle_reso):
x, y, angle, r = [], [], [], []
# points sampling
for theta in np.arange(0.0, 2.0 * math.pi, angle_reso):
nx = cx + cr * math.cos(theta)
ny = cy + cr * math.sin(theta)
nangle = math.atan2(ny, nx)
nr = math.hypot(nx, ny) * random.uniform(0.95, 1.05)
x.append(nx)
y.append(ny)
angle.append(nangle)
r.append(nr)
# ray casting filter
rx, ry = ray_casting_filter(x, y, angle, r, angle_reso)
return rx, ry
| 511 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/circle_fitting/circle_fitting.py
|
ray_casting_filter
|
(xl, yl, thetal, rangel, angle_reso)
|
return rx, ry
| 73 | 90 |
def ray_casting_filter(xl, yl, thetal, rangel, angle_reso):
rx, ry = [], []
rangedb = [float("inf") for _ in range(
int(math.floor((math.pi * 2.0) / angle_reso)) + 1)]
for i, _ in enumerate(thetal):
angleid = math.floor(thetal[i] / angle_reso)
if rangedb[angleid] > rangel[i]:
rangedb[angleid] = rangel[i]
for i, _ in enumerate(rangedb):
t = i * angle_reso
if rangedb[i] != float("inf"):
rx.append(rangedb[i] * math.cos(t))
ry.append(rangedb[i] * math.sin(t))
return rx, ry
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/circle_fitting/circle_fitting.py#L73-L90
| 2 |
[
0,
1,
2,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
] | 94.444444 |
[] | 0 | false | 98.4375 | 18 | 6 | 100 | 0 |
def ray_casting_filter(xl, yl, thetal, rangel, angle_reso):
rx, ry = [], []
rangedb = [float("inf") for _ in range(
int(math.floor((math.pi * 2.0) / angle_reso)) + 1)]
for i, _ in enumerate(thetal):
angleid = math.floor(thetal[i] / angle_reso)
if rangedb[angleid] > rangel[i]:
rangedb[angleid] = rangel[i]
for i, _ in enumerate(rangedb):
t = i * angle_reso
if rangedb[i] != float("inf"):
rx.append(rangedb[i] * math.cos(t))
ry.append(rangedb[i] * math.sin(t))
return rx, ry
| 512 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/circle_fitting/circle_fitting.py
|
plot_circle
|
(x, y, size, color="-b")
| 93 | 98 |
def plot_circle(x, y, size, color="-b"): # pragma: no cover
deg = list(range(0, 360, 5))
deg.append(0)
xl = [x + size * math.cos(np.deg2rad(d)) for d in deg]
yl = [y + size * math.sin(np.deg2rad(d)) for d in deg]
plt.plot(xl, yl, color)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/circle_fitting/circle_fitting.py#L93-L98
| 2 |
[] | 0 |
[] | 0 | false | 98.4375 | 6 | 3 | 100 | 0 |
def plot_circle(x, y, size, color="-b"): # pragma: no cover
deg = list(range(0, 360, 5))
deg.append(0)
xl = [x + size * math.cos(np.deg2rad(d)) for d in deg]
yl = [y + size * math.sin(np.deg2rad(d)) for d in deg]
plt.plot(xl, yl, color)
| 513 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Mapping/circle_fitting/circle_fitting.py
|
main
|
()
| 101 | 137 |
def main():
# simulation parameters
simtime = 15.0 # simulation time
dt = 1.0 # time tick
cx = -2.0 # initial x position of obstacle
cy = -8.0 # initial y position of obstacle
cr = 1.0 # obstacle radious
theta = np.deg2rad(30.0) # obstacle moving direction
angle_reso = np.deg2rad(3.0) # sensor angle resolution
time = 0.0
while time <= simtime:
time += dt
cx += math.cos(theta)
cy += math.cos(theta)
x, y = get_sample_points(cx, cy, cr, angle_reso)
ex, ey, er, error = circle_fitting(x, y)
print("Error:", error)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.axis("equal")
plt.plot(0.0, 0.0, "*r")
plot_circle(cx, cy, cr)
plt.plot(x, y, "xr")
plot_circle(ex, ey, er, "-r")
plt.pause(dt)
print("Done")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Mapping/circle_fitting/circle_fitting.py#L101-L137
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
35,
36
] | 92.857143 |
[] | 0 | false | 98.4375 | 37 | 3 | 100 | 0 |
def main():
# simulation parameters
simtime = 15.0 # simulation time
dt = 1.0 # time tick
cx = -2.0 # initial x position of obstacle
cy = -8.0 # initial y position of obstacle
cr = 1.0 # obstacle radious
theta = np.deg2rad(30.0) # obstacle moving direction
angle_reso = np.deg2rad(3.0) # sensor angle resolution
time = 0.0
while time <= simtime:
time += dt
cx += math.cos(theta)
cy += math.cos(theta)
x, y = get_sample_points(cx, cy, cr, angle_reso)
ex, ey, er, error = circle_fitting(x, y)
print("Error:", error)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.axis("equal")
plt.plot(0.0, 0.0, "*r")
plot_circle(cx, cy, cr)
plt.plot(x, y, "xr")
plot_circle(ex, ey, er, "-r")
plt.pause(dt)
print("Done")
| 514 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
calc_input
|
()
|
return u
| 37 | 41 |
def calc_input():
v = 1.0 # [m/s]
yawrate = 0.1 # [rad/s]
u = np.array([[v], [yawrate]])
return u
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L37-L41
| 2 |
[
0,
1,
2,
3,
4
] | 100 |
[] | 0 | true | 87.356322 | 5 | 1 | 100 | 0 |
def calc_input():
v = 1.0 # [m/s]
yawrate = 0.1 # [rad/s]
u = np.array([[v], [yawrate]])
return u
| 515 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
observation
|
(xTrue, xd, u)
|
return xTrue, z, xd, ud
| 44 | 55 |
def observation(xTrue, xd, u):
xTrue = motion_model(xTrue, u)
# add noise to gps x-y
z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1)
# add noise to input
ud = u + INPUT_NOISE @ np.random.randn(2, 1)
xd = motion_model(xd, ud)
return xTrue, z, xd, ud
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L44-L55
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11
] | 100 |
[] | 0 | true | 87.356322 | 12 | 1 | 100 | 0 |
def observation(xTrue, xd, u):
xTrue = motion_model(xTrue, u)
# add noise to gps x-y
z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1)
# add noise to input
ud = u + INPUT_NOISE @ np.random.randn(2, 1)
xd = motion_model(xd, ud)
return xTrue, z, xd, ud
| 516 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
motion_model
|
(x, u)
|
return x
| 58 | 71 |
def motion_model(x, u):
F = np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
B = np.array([[DT * math.cos(x[2, 0]), 0],
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
x = F @ x + B @ u
return x
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L58-L71
| 2 |
[
0,
1,
5,
6,
10,
11,
12,
13
] | 57.142857 |
[] | 0 | false | 87.356322 | 14 | 1 | 100 | 0 |
def motion_model(x, u):
F = np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
B = np.array([[DT * math.cos(x[2, 0]), 0],
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
x = F @ x + B @ u
return x
| 517 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
observation_model
|
(x)
|
return z
| 74 | 82 |
def observation_model(x):
H = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0]
])
z = H @ x
return z
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L74-L82
| 2 |
[
0,
1,
5,
6,
7,
8
] | 66.666667 |
[] | 0 | false | 87.356322 | 9 | 1 | 100 | 0 |
def observation_model(x):
H = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0]
])
z = H @ x
return z
| 518 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
jacob_f
|
(x, u)
|
return jF
|
Jacobian of Motion Model
motion model
x_{t+1} = x_t+v*dt*cos(yaw)
y_{t+1} = y_t+v*dt*sin(yaw)
yaw_{t+1} = yaw_t+omega*dt
v_{t+1} = v{t}
so
dx/dyaw = -v*dt*sin(yaw)
dx/dv = dt*cos(yaw)
dy/dyaw = v*dt*cos(yaw)
dy/dv = dt*sin(yaw)
|
Jacobian of Motion Model
| 85 | 108 |
def jacob_f(x, u):
"""
Jacobian of Motion Model
motion model
x_{t+1} = x_t+v*dt*cos(yaw)
y_{t+1} = y_t+v*dt*sin(yaw)
yaw_{t+1} = yaw_t+omega*dt
v_{t+1} = v{t}
so
dx/dyaw = -v*dt*sin(yaw)
dx/dv = dt*cos(yaw)
dy/dyaw = v*dt*cos(yaw)
dy/dv = dt*sin(yaw)
"""
yaw = x[2, 0]
v = u[0, 0]
jF = np.array([
[1.0, 0.0, -DT * v * math.sin(yaw), DT * math.cos(yaw)],
[0.0, 1.0, DT * v * math.cos(yaw), DT * math.sin(yaw)],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]])
return jF
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L85-L108
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | 100 |
[] | 0 | true | 87.356322 | 24 | 1 | 100 | 12 |
def jacob_f(x, u):
yaw = x[2, 0]
v = u[0, 0]
jF = np.array([
[1.0, 0.0, -DT * v * math.sin(yaw), DT * math.cos(yaw)],
[0.0, 1.0, DT * v * math.cos(yaw), DT * math.sin(yaw)],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]])
return jF
| 519 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
jacob_h
|
()
|
return jH
| 111 | 118 |
def jacob_h():
# Jacobian of Observation Model
jH = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0]
])
return jH
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L111-L118
| 2 |
[
0,
1,
2,
6,
7
] | 62.5 |
[] | 0 | false | 87.356322 | 8 | 1 | 100 | 0 |
def jacob_h():
# Jacobian of Observation Model
jH = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0]
])
return jH
| 520 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
ekf_estimation
|
(xEst, PEst, z, u)
|
return xEst, PEst
| 121 | 135 |
def ekf_estimation(xEst, PEst, z, u):
# Predict
xPred = motion_model(xEst, u)
jF = jacob_f(xEst, u)
PPred = jF @ PEst @ jF.T + Q
# Update
jH = jacob_h()
zPred = observation_model(xPred)
y = z - zPred
S = jH @ PPred @ jH.T + R
K = PPred @ jH.T @ np.linalg.inv(S)
xEst = xPred + K @ y
PEst = (np.eye(len(xEst)) - K @ jH) @ PPred
return xEst, PEst
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L121-L135
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14
] | 100 |
[] | 0 | true | 87.356322 | 15 | 1 | 100 | 0 |
def ekf_estimation(xEst, PEst, z, u):
# Predict
xPred = motion_model(xEst, u)
jF = jacob_f(xEst, u)
PPred = jF @ PEst @ jF.T + Q
# Update
jH = jacob_h()
zPred = observation_model(xPred)
y = z - zPred
S = jH @ PPred @ jH.T + R
K = PPred @ jH.T @ np.linalg.inv(S)
xEst = xPred + K @ y
PEst = (np.eye(len(xEst)) - K @ jH) @ PPred
return xEst, PEst
| 521 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
plot_covariance_ellipse
|
(xEst, PEst)
| 138 | 158 |
def plot_covariance_ellipse(xEst, PEst): # pragma: no cover
Pxy = PEst[0:2, 0:2]
eigval, eigvec = np.linalg.eig(Pxy)
if eigval[0] >= eigval[1]:
bigind = 0
smallind = 1
else:
bigind = 1
smallind = 0
t = np.arange(0, 2 * math.pi + 0.1, 0.1)
a = math.sqrt(eigval[bigind])
b = math.sqrt(eigval[smallind])
x = [a * math.cos(it) for it in t]
y = [b * math.sin(it) for it in t]
angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
fx = rot_mat_2d(angle) @ (np.array([x, y]))
px = np.array(fx[0, :] + xEst[0, 0]).flatten()
py = np.array(fx[1, :] + xEst[1, 0]).flatten()
plt.plot(px, py, "--r")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L138-L158
| 2 |
[] | 0 |
[] | 0 | false | 87.356322 | 21 | 4 | 100 | 0 |
def plot_covariance_ellipse(xEst, PEst): # pragma: no cover
Pxy = PEst[0:2, 0:2]
eigval, eigvec = np.linalg.eig(Pxy)
if eigval[0] >= eigval[1]:
bigind = 0
smallind = 1
else:
bigind = 1
smallind = 0
t = np.arange(0, 2 * math.pi + 0.1, 0.1)
a = math.sqrt(eigval[bigind])
b = math.sqrt(eigval[smallind])
x = [a * math.cos(it) for it in t]
y = [b * math.sin(it) for it in t]
angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
fx = rot_mat_2d(angle) @ (np.array([x, y]))
px = np.array(fx[0, :] + xEst[0, 0]).flatten()
py = np.array(fx[1, :] + xEst[1, 0]).flatten()
plt.plot(px, py, "--r")
| 522 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/extended_kalman_filter/extended_kalman_filter.py
|
main
|
()
| 161 | 208 |
def main():
print(__file__ + " start!!")
time = 0.0
# State Vector [x y yaw v]'
xEst = np.zeros((4, 1))
xTrue = np.zeros((4, 1))
PEst = np.eye(4)
xDR = np.zeros((4, 1)) # Dead reckoning
# history
hxEst = xEst
hxTrue = xTrue
hxDR = xTrue
hz = np.zeros((2, 1))
while SIM_TIME >= time:
time += DT
u = calc_input()
xTrue, z, xDR, ud = observation(xTrue, xDR, u)
xEst, PEst = ekf_estimation(xEst, PEst, z, ud)
# store data history
hxEst = np.hstack((hxEst, xEst))
hxDR = np.hstack((hxDR, xDR))
hxTrue = np.hstack((hxTrue, xTrue))
hz = np.hstack((hz, z))
if show_animation:
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.plot(hz[0, :], hz[1, :], ".g")
plt.plot(hxTrue[0, :].flatten(),
hxTrue[1, :].flatten(), "-b")
plt.plot(hxDR[0, :].flatten(),
hxDR[1, :].flatten(), "-k")
plt.plot(hxEst[0, :].flatten(),
hxEst[1, :].flatten(), "-r")
plot_covariance_ellipse(xEst, PEst)
plt.axis("equal")
plt.grid(True)
plt.pause(0.001)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/extended_kalman_filter/extended_kalman_filter.py#L161-L208
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32
] | 68.75 |
[
33,
35,
37,
38,
40,
42,
44,
45,
46,
47
] | 20.833333 | false | 87.356322 | 48 | 3 | 79.166667 | 0 |
def main():
print(__file__ + " start!!")
time = 0.0
# State Vector [x y yaw v]'
xEst = np.zeros((4, 1))
xTrue = np.zeros((4, 1))
PEst = np.eye(4)
xDR = np.zeros((4, 1)) # Dead reckoning
# history
hxEst = xEst
hxTrue = xTrue
hxDR = xTrue
hz = np.zeros((2, 1))
while SIM_TIME >= time:
time += DT
u = calc_input()
xTrue, z, xDR, ud = observation(xTrue, xDR, u)
xEst, PEst = ekf_estimation(xEst, PEst, z, ud)
# store data history
hxEst = np.hstack((hxEst, xEst))
hxDR = np.hstack((hxDR, xDR))
hxTrue = np.hstack((hxTrue, xTrue))
hz = np.hstack((hz, z))
if show_animation:
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.plot(hz[0, :], hz[1, :], ".g")
plt.plot(hxTrue[0, :].flatten(),
hxTrue[1, :].flatten(), "-b")
plt.plot(hxDR[0, :].flatten(),
hxDR[1, :].flatten(), "-k")
plt.plot(hxEst[0, :].flatten(),
hxEst[1, :].flatten(), "-r")
plot_covariance_ellipse(xEst, PEst)
plt.axis("equal")
plt.grid(True)
plt.pause(0.001)
| 523 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
cubature_kalman_filter
|
(x_est, p_est, z)
|
return x_upd, p_upd
| 88 | 91 |
def cubature_kalman_filter(x_est, p_est, z):
x_pred, p_pred = cubature_prediction(x_est, p_est)
x_upd, p_upd = cubature_update(x_pred, p_pred, z)
return x_upd, p_upd
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L88-L91
| 2 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 72.727273 | 4 | 1 | 100 | 0 |
def cubature_kalman_filter(x_est, p_est, z):
x_pred, p_pred = cubature_prediction(x_est, p_est)
x_upd, p_upd = cubature_update(x_pred, p_pred, z)
return x_upd, p_upd
| 524 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
f
|
(x)
|
return x
|
Motion Model
References:
http://fusion.isif.org/proceedings/fusion08CD/papers/1569107835.pdf
https://github.com/balzer82/Kalman
|
Motion Model
References:
http://fusion.isif.org/proceedings/fusion08CD/papers/1569107835.pdf
https://github.com/balzer82/Kalman
| 94 | 106 |
def f(x):
"""
Motion Model
References:
http://fusion.isif.org/proceedings/fusion08CD/papers/1569107835.pdf
https://github.com/balzer82/Kalman
"""
x[0] = x[0] + (x[3]/x[4]) * (np.sin(x[4] * dt + x[2]) - np.sin(x[2]))
x[1] = x[1] + (x[3]/x[4]) * (- np.cos(x[4] * dt + x[2]) + np.cos(x[2]))
x[2] = x[2] + x[4] * dt
x[3] = x[3]
x[4] = x[4]
return x
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L94-L106
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 72.727273 | 13 | 1 | 100 | 4 |
def f(x):
x[0] = x[0] + (x[3]/x[4]) * (np.sin(x[4] * dt + x[2]) - np.sin(x[2]))
x[1] = x[1] + (x[3]/x[4]) * (- np.cos(x[4] * dt + x[2]) + np.cos(x[2]))
x[2] = x[2] + x[4] * dt
x[3] = x[3]
x[4] = x[4]
return x
| 525 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
h
|
(x)
|
return x
|
Measurement Model
|
Measurement Model
| 109 | 112 |
def h(x):
"""Measurement Model"""
x = hx @ x
return x
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L109-L112
| 2 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 72.727273 | 4 | 1 | 100 | 1 |
def h(x):
x = hx @ x
return x
| 526 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
sigma
|
(x, p)
|
return SP, W
|
Unscented Transform with Cubature Rule
Generate 2n Sigma Points to represent the nonlinear motion
Assign Weights to each Sigma Point, Wi = 1/2n
Cubature Rule - Special Case of Unscented Transform
W0 = 0, no extra tuning parameters, no negative weights
|
Unscented Transform with Cubature Rule
Generate 2n Sigma Points to represent the nonlinear motion
Assign Weights to each Sigma Point, Wi = 1/2n
Cubature Rule - Special Case of Unscented Transform
W0 = 0, no extra tuning parameters, no negative weights
| 115 | 132 |
def sigma(x, p):
"""
Unscented Transform with Cubature Rule
Generate 2n Sigma Points to represent the nonlinear motion
Assign Weights to each Sigma Point, Wi = 1/2n
Cubature Rule - Special Case of Unscented Transform
W0 = 0, no extra tuning parameters, no negative weights
"""
n = np.shape(x)[0]
SP = np.zeros((n, 2*n))
W = np.zeros((1, 2*n))
for i in range(n):
SD = sqrtm(p)
SP[:, i] = (x + (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
SP[:, i+n] = (x - (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
W[:, i] = 1/(2*n)
W[:, i+n] = W[:, i]
return SP, W
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L115-L132
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
] | 100 |
[] | 0 | true | 72.727273 | 18 | 2 | 100 | 5 |
def sigma(x, p):
n = np.shape(x)[0]
SP = np.zeros((n, 2*n))
W = np.zeros((1, 2*n))
for i in range(n):
SD = sqrtm(p)
SP[:, i] = (x + (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
SP[:, i+n] = (x - (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
W[:, i] = 1/(2*n)
W[:, i+n] = W[:, i]
return SP, W
| 527 |
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
cubature_prediction
|
(x_pred, p_pred)
|
return x_pred, p_pred
| 135 | 145 |
def cubature_prediction(x_pred, p_pred):
n = np.shape(x_pred)[0]
[SP, W] = sigma(x_pred, p_pred)
x_pred = np.zeros((n, 1))
p_pred = q
for i in range(2*n):
x_pred = x_pred + (f(SP[:, i]).reshape((n, 1)) * W[0, i])
for i in range(2*n):
p_step = (f(SP[:, i]).reshape((n, 1)) - x_pred)
p_pred = p_pred + (p_step @ p_step.T * W[0, i])
return x_pred, p_pred
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L135-L145
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
] | 100 |
[] | 0 | true | 72.727273 | 11 | 3 | 100 | 0 |
def cubature_prediction(x_pred, p_pred):
n = np.shape(x_pred)[0]
[SP, W] = sigma(x_pred, p_pred)
x_pred = np.zeros((n, 1))
p_pred = q
for i in range(2*n):
x_pred = x_pred + (f(SP[:, i]).reshape((n, 1)) * W[0, i])
for i in range(2*n):
p_step = (f(SP[:, i]).reshape((n, 1)) - x_pred)
p_pred = p_pred + (p_step @ p_step.T * W[0, i])
return x_pred, p_pred
| 528 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
cubature_update
|
(x_pred, p_pred, z)
|
return x_pred, p_pred
| 148 | 164 |
def cubature_update(x_pred, p_pred, z):
n = np.shape(x_pred)[0]
m = np.shape(z)[0]
[SP, W] = sigma(x_pred, p_pred)
y_k = np.zeros((m, 1))
P_xy = np.zeros((n, m))
s = r
for i in range(2*n):
y_k = y_k + (h(SP[:, i]).reshape((m, 1)) * W[0, i])
for i in range(2*n):
p_step = (h(SP[:, i]).reshape((m, 1)) - y_k)
P_xy = P_xy + ((SP[:, i]).reshape((n, 1)) -
x_pred) @ p_step.T * W[0, i]
s = s + p_step @ p_step.T * W[0, i]
x_pred = x_pred + P_xy @ np.linalg.pinv(s) @ (z - y_k)
p_pred = p_pred - P_xy @ np.linalg.pinv(s) @ P_xy.T
return x_pred, p_pred
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L148-L164
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
13,
14,
15,
16
] | 94.117647 |
[] | 0 | false | 72.727273 | 17 | 3 | 100 | 0 |
def cubature_update(x_pred, p_pred, z):
n = np.shape(x_pred)[0]
m = np.shape(z)[0]
[SP, W] = sigma(x_pred, p_pred)
y_k = np.zeros((m, 1))
P_xy = np.zeros((n, m))
s = r
for i in range(2*n):
y_k = y_k + (h(SP[:, i]).reshape((m, 1)) * W[0, i])
for i in range(2*n):
p_step = (h(SP[:, i]).reshape((m, 1)) - y_k)
P_xy = P_xy + ((SP[:, i]).reshape((n, 1)) -
x_pred) @ p_step.T * W[0, i]
s = s + p_step @ p_step.T * W[0, i]
x_pred = x_pred + P_xy @ np.linalg.pinv(s) @ (z - y_k)
p_pred = p_pred - P_xy @ np.linalg.pinv(s) @ P_xy.T
return x_pred, p_pred
| 529 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
generate_measurement
|
(x_true)
|
return z
| 167 | 170 |
def generate_measurement(x_true):
gz = hx @ x_true
z = gz + z_noise @ np.random.randn(4, 1)
return z
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L167-L170
| 2 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 72.727273 | 4 | 1 | 100 | 0 |
def generate_measurement(x_true):
gz = hx @ x_true
z = gz + z_noise @ np.random.randn(4, 1)
return z
| 530 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
plot_animation
|
(i, x_true_cat, x_est_cat, z)
| 173 | 182 |
def plot_animation(i, x_true_cat, x_est_cat, z):
if i == 0:
plt.plot(x_true_cat[0], x_true_cat[1], '.r')
plt.plot(x_est_cat[0], x_est_cat[1], '.b')
else:
plt.plot(x_true_cat[0:, 0], x_true_cat[0:, 1], 'r')
plt.plot(x_est_cat[0:, 0], x_est_cat[0:, 1], 'b')
plt.plot(z[0], z[1], '+g')
plt.grid(True)
plt.pause(0.001)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L173-L182
| 2 |
[
0
] | 10 |
[
1,
2,
3,
5,
6,
7,
8,
9
] | 80 | false | 72.727273 | 10 | 2 | 20 | 0 |
def plot_animation(i, x_true_cat, x_est_cat, z):
if i == 0:
plt.plot(x_true_cat[0], x_true_cat[1], '.r')
plt.plot(x_est_cat[0], x_est_cat[1], '.b')
else:
plt.plot(x_true_cat[0:, 0], x_true_cat[0:, 1], 'r')
plt.plot(x_est_cat[0:, 0], x_est_cat[0:, 1], 'b')
plt.plot(z[0], z[1], '+g')
plt.grid(True)
plt.pause(0.001)
| 531 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
plot_ellipse
|
(x_est, p_est)
| 185 | 198 |
def plot_ellipse(x_est, p_est):
phi = np.linspace(0, 2 * math.pi, 100)
p_ellipse = np.array(
[[p_est[0, 0], p_est[0, 1]], [p_est[1, 0], p_est[1, 1]]])
x0 = 3 * sqrtm(p_ellipse)
xy_1 = np.array([])
xy_2 = np.array([])
for i in range(100):
arr = np.array([[math.sin(phi[i])], [math.cos(phi[i])]])
arr = x0 @ arr
xy_1 = np.hstack([xy_1, arr[0]])
xy_2 = np.hstack([xy_2, arr[1]])
plt.plot(xy_1 + x_est[0], xy_2 + x_est[1], 'r')
plt.pause(0.00001)
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L185-L198
| 2 |
[
0
] | 7.142857 |
[
1,
2,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13
] | 85.714286 | false | 72.727273 | 14 | 2 | 14.285714 | 0 |
def plot_ellipse(x_est, p_est):
phi = np.linspace(0, 2 * math.pi, 100)
p_ellipse = np.array(
[[p_est[0, 0], p_est[0, 1]], [p_est[1, 0], p_est[1, 1]]])
x0 = 3 * sqrtm(p_ellipse)
xy_1 = np.array([])
xy_2 = np.array([])
for i in range(100):
arr = np.array([[math.sin(phi[i])], [math.cos(phi[i])]])
arr = x0 @ arr
xy_1 = np.hstack([xy_1, arr[0]])
xy_2 = np.hstack([xy_2, arr[1]])
plt.plot(xy_1 + x_est[0], xy_2 + x_est[1], 'r')
plt.pause(0.00001)
| 532 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
plot_final
|
(x_true_cat, x_est_cat, z_cat)
| 201 | 214 |
def plot_final(x_true_cat, x_est_cat, z_cat):
fig = plt.figure()
subplot = fig.add_subplot(111)
subplot.plot(x_true_cat[0:, 0], x_true_cat[0:, 1],
'r', label='True Position')
subplot.plot(x_est_cat[0:, 0], x_est_cat[0:, 1],
'b', label='Estimated Position')
subplot.plot(z_cat[0:, 0], z_cat[0:, 1], '+g', label='Noisy Measurements')
subplot.set_xlabel('x [m]')
subplot.set_ylabel('y [m]')
subplot.set_title('Cubature Kalman Filter - CTRV Model')
subplot.legend(loc='upper left', shadow=True, fontsize='large')
plt.grid(True)
plt.show()
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L201-L214
| 2 |
[
0
] | 7.142857 |
[
1,
2,
3,
5,
7,
8,
9,
10,
11,
12,
13
] | 78.571429 | false | 72.727273 | 14 | 1 | 21.428571 | 0 |
def plot_final(x_true_cat, x_est_cat, z_cat):
fig = plt.figure()
subplot = fig.add_subplot(111)
subplot.plot(x_true_cat[0:, 0], x_true_cat[0:, 1],
'r', label='True Position')
subplot.plot(x_est_cat[0:, 0], x_est_cat[0:, 1],
'b', label='Estimated Position')
subplot.plot(z_cat[0:, 0], z_cat[0:, 1], '+g', label='Noisy Measurements')
subplot.set_xlabel('x [m]')
subplot.set_ylabel('y [m]')
subplot.set_title('Cubature Kalman Filter - CTRV Model')
subplot.legend(loc='upper left', shadow=True, fontsize='large')
plt.grid(True)
plt.show()
| 533 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/cubature_kalman_filter/cubature_kalman_filter.py
|
main
|
()
| 217 | 242 |
def main():
print(__file__ + " start!!")
x_est = x_0
p_est = p_0
x_true = x_0
x_true_cat = np.array([x_0[0, 0], x_0[1, 0]])
x_est_cat = np.array([x_0[0, 0], x_0[1, 0]])
z_cat = np.array([x_0[0, 0], x_0[1, 0]])
for i in range(N):
x_true = f(x_true)
z = generate_measurement(x_true)
if i == (N - 1) and show_final == 1:
show_final_flag = 1
else:
show_final_flag = 0
if show_animation == 1:
plot_animation(i, x_true_cat, x_est_cat, z)
if show_ellipse == 1:
plot_ellipse(x_est[0:2], p_est)
if show_final_flag == 1:
plot_final(x_true_cat, x_est_cat, z_cat)
x_est, p_est = cubature_kalman_filter(x_est, p_est, z)
x_true_cat = np.vstack((x_true_cat, x_true[0:2].T))
x_est_cat = np.vstack((x_est_cat, x_est[0:2].T))
z_cat = np.vstack((z_cat, z[0:2].T))
print('CKF Over')
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/cubature_kalman_filter/cubature_kalman_filter.py#L217-L242
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
14,
15,
17,
19,
21,
22,
23,
24,
25
] | 80.769231 |
[
12,
16,
18,
20
] | 15.384615 | false | 72.727273 | 26 | 7 | 84.615385 | 0 |
def main():
print(__file__ + " start!!")
x_est = x_0
p_est = p_0
x_true = x_0
x_true_cat = np.array([x_0[0, 0], x_0[1, 0]])
x_est_cat = np.array([x_0[0, 0], x_0[1, 0]])
z_cat = np.array([x_0[0, 0], x_0[1, 0]])
for i in range(N):
x_true = f(x_true)
z = generate_measurement(x_true)
if i == (N - 1) and show_final == 1:
show_final_flag = 1
else:
show_final_flag = 0
if show_animation == 1:
plot_animation(i, x_true_cat, x_est_cat, z)
if show_ellipse == 1:
plot_ellipse(x_est[0:2], p_est)
if show_final_flag == 1:
plot_final(x_true_cat, x_est_cat, z_cat)
x_est, p_est = cubature_kalman_filter(x_est, p_est, z)
x_true_cat = np.vstack((x_true_cat, x_true[0:2].T))
x_est_cat = np.vstack((x_est_cat, x_est[0:2].T))
z_cat = np.vstack((z_cat, z[0:2].T))
print('CKF Over')
| 534 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
histogram_filter_localization
|
(grid_map, u, z, yaw)
|
return grid_map
| 59 | 64 |
def histogram_filter_localization(grid_map, u, z, yaw):
grid_map = motion_update(grid_map, u, yaw)
grid_map = observation_update(grid_map, z, RANGE_STD)
return grid_map
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L59-L64
| 2 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 90.410959 | 6 | 1 | 100 | 0 |
def histogram_filter_localization(grid_map, u, z, yaw):
grid_map = motion_update(grid_map, u, yaw)
grid_map = observation_update(grid_map, z, RANGE_STD)
return grid_map
| 535 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
calc_gaussian_observation_pdf
|
(grid_map, z, iz, ix, iy, std)
|
return pdf
| 67 | 76 |
def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std):
# predicted range
x = ix * grid_map.xy_resolution + grid_map.min_x
y = iy * grid_map.xy_resolution + grid_map.min_y
d = math.hypot(x - z[iz, 1], y - z[iz, 2])
# likelihood
pdf = norm.pdf(d - z[iz, 0], 0.0, std)
return pdf
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L67-L76
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
] | 100 |
[] | 0 | true | 90.410959 | 10 | 1 | 100 | 0 |
def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std):
# predicted range
x = ix * grid_map.xy_resolution + grid_map.min_x
y = iy * grid_map.xy_resolution + grid_map.min_y
d = math.hypot(x - z[iz, 1], y - z[iz, 2])
# likelihood
pdf = norm.pdf(d - z[iz, 0], 0.0, std)
return pdf
| 536 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
observation_update
|
(grid_map, z, std)
|
return grid_map
| 79 | 88 |
def observation_update(grid_map, z, std):
for iz in range(z.shape[0]):
for ix in range(grid_map.x_w):
for iy in range(grid_map.y_w):
grid_map.data[ix][iy] *= calc_gaussian_observation_pdf(
grid_map, z, iz, ix, iy, std)
grid_map = normalize_probability(grid_map)
return grid_map
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L79-L88
| 2 |
[
0,
1,
2,
3,
4,
6,
7,
8,
9
] | 90 |
[] | 0 | false | 90.410959 | 10 | 4 | 100 | 0 |
def observation_update(grid_map, z, std):
for iz in range(z.shape[0]):
for ix in range(grid_map.x_w):
for iy in range(grid_map.y_w):
grid_map.data[ix][iy] *= calc_gaussian_observation_pdf(
grid_map, z, iz, ix, iy, std)
grid_map = normalize_probability(grid_map)
return grid_map
| 537 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
calc_control_input
|
()
|
return u
| 91 | 95 |
def calc_control_input():
v = 1.0 # [m/s]
yaw_rate = 0.1 # [rad/s]
u = np.array([v, yaw_rate]).reshape(2, 1)
return u
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L91-L95
| 2 |
[
0,
1,
2,
3,
4
] | 100 |
[] | 0 | true | 90.410959 | 5 | 1 | 100 | 0 |
def calc_control_input():
v = 1.0 # [m/s]
yaw_rate = 0.1 # [rad/s]
u = np.array([v, yaw_rate]).reshape(2, 1)
return u
| 538 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
motion_model
|
(x, u)
|
return x
| 98 | 111 |
def motion_model(x, u):
F = np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
B = np.array([[DT * math.cos(x[2, 0]), 0],
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
x = F @ x + B @ u
return x
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L98-L111
| 2 |
[
0,
1,
5,
6,
10,
11,
12,
13
] | 57.142857 |
[] | 0 | false | 90.410959 | 14 | 1 | 100 | 0 |
def motion_model(x, u):
F = np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
B = np.array([[DT * math.cos(x[2, 0]), 0],
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
x = F @ x + B @ u
return x
| 539 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
draw_heat_map
|
(data, mx, my)
| 114 | 118 |
def draw_heat_map(data, mx, my):
max_value = max([max(i_data) for i_data in data])
plt.grid(False)
plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues"))
plt.axis("equal")
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L114-L118
| 2 |
[
0
] | 20 |
[
1,
2,
3,
4
] | 80 | false | 90.410959 | 5 | 2 | 20 | 0 |
def draw_heat_map(data, mx, my):
max_value = max([max(i_data) for i_data in data])
plt.grid(False)
plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues"))
plt.axis("equal")
| 540 |
|||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
observation
|
(xTrue, u, RFID)
|
return xTrue, z, ud
| 121 | 141 |
def observation(xTrue, u, RFID):
xTrue = motion_model(xTrue, u)
z = np.zeros((0, 3))
for i in range(len(RFID[:, 0])):
dx = xTrue[0, 0] - RFID[i, 0]
dy = xTrue[1, 0] - RFID[i, 1]
d = math.hypot(dx, dy)
if d <= MAX_RANGE:
# add noise to range observation
dn = d + np.random.randn() * NOISE_RANGE
zi = np.array([dn, RFID[i, 0], RFID[i, 1]])
z = np.vstack((z, zi))
# add noise to speed
ud = u[:, :]
ud[0] += np.random.randn() * NOISE_SPEED
return xTrue, z, ud
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L121-L141
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
] | 100 |
[] | 0 | true | 90.410959 | 21 | 3 | 100 | 0 |
def observation(xTrue, u, RFID):
xTrue = motion_model(xTrue, u)
z = np.zeros((0, 3))
for i in range(len(RFID[:, 0])):
dx = xTrue[0, 0] - RFID[i, 0]
dy = xTrue[1, 0] - RFID[i, 1]
d = math.hypot(dx, dy)
if d <= MAX_RANGE:
# add noise to range observation
dn = d + np.random.randn() * NOISE_RANGE
zi = np.array([dn, RFID[i, 0], RFID[i, 1]])
z = np.vstack((z, zi))
# add noise to speed
ud = u[:, :]
ud[0] += np.random.randn() * NOISE_SPEED
return xTrue, z, ud
| 541 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
normalize_probability
|
(grid_map)
|
return grid_map
| 144 | 151 |
def normalize_probability(grid_map):
sump = sum([sum(i_data) for i_data in grid_map.data])
for ix in range(grid_map.x_w):
for iy in range(grid_map.y_w):
grid_map.data[ix][iy] /= sump
return grid_map
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L144-L151
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7
] | 100 |
[] | 0 | true | 90.410959 | 8 | 4 | 100 | 0 |
def normalize_probability(grid_map):
sump = sum([sum(i_data) for i_data in grid_map.data])
for ix in range(grid_map.x_w):
for iy in range(grid_map.y_w):
grid_map.data[ix][iy] /= sump
return grid_map
| 542 |
||
AtsushiSakai/PythonRobotics
|
15ab19688b2f6c03ee91a853f1f8cc9def84d162
|
Localization/histogram_filter/histogram_filter.py
|
init_grid_map
|
(xy_resolution, min_x, min_y, max_x, max_y)
|
return grid_map
| 154 | 171 |
def init_grid_map(xy_resolution, min_x, min_y, max_x, max_y):
grid_map = GridMap()
grid_map.xy_resolution = xy_resolution
grid_map.min_x = min_x
grid_map.min_y = min_y
grid_map.max_x = max_x
grid_map.max_y = max_y
grid_map.x_w = int(round((grid_map.max_x - grid_map.min_x)
/ grid_map.xy_resolution))
grid_map.y_w = int(round((grid_map.max_y - grid_map.min_y)
/ grid_map.xy_resolution))
grid_map.data = [[1.0 for _ in range(grid_map.y_w)]
for _ in range(grid_map.x_w)]
grid_map = normalize_probability(grid_map)
return grid_map
|
https://github.com/AtsushiSakai/PythonRobotics/blob/15ab19688b2f6c03ee91a853f1f8cc9def84d162/project2/Localization/histogram_filter/histogram_filter.py#L154-L171
| 2 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
10,
12,
13,
15,
16,
17
] | 83.333333 |
[] | 0 | false | 90.410959 | 18 | 3 | 100 | 0 |
def init_grid_map(xy_resolution, min_x, min_y, max_x, max_y):
grid_map = GridMap()
grid_map.xy_resolution = xy_resolution
grid_map.min_x = min_x
grid_map.min_y = min_y
grid_map.max_x = max_x
grid_map.max_y = max_y
grid_map.x_w = int(round((grid_map.max_x - grid_map.min_x)
/ grid_map.xy_resolution))
grid_map.y_w = int(round((grid_map.max_y - grid_map.min_y)
/ grid_map.xy_resolution))
grid_map.data = [[1.0 for _ in range(grid_map.y_w)]
for _ in range(grid_map.x_w)]
grid_map = normalize_probability(grid_map)
return grid_map
| 543 |
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