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## Valve Index datasets
These datasets were recorded using a Valve Index with the `vive` driver in
Monado and they have ground truth from 3 lighthouses tracking the headset through
the proprietary OpenVR implementation provided by SteamVR. The exact commit used
in Monado at the time of recording is
[a4e7765d](https://gitlab.freedesktop.org/mateosss/monado/-/commit/a4e7765d7219b06a0c801c7bb33f56d3ea69229d).
The datasets are in the ASL dataset format, the same as the [EuRoC
datasets](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets).
Besides the main EuRoC format files, we provide some extra files with raw
timestamp data for exploring real time timestamp alignment techniques.
The dataset is post-processed to reduce as much as possible special treatment
from SLAM systems: camera-IMU and ground truth-IMU timestamp alignment, IMU
alignment and bias calibration have been applied, lighthouse tracked pose has
been converted to IMU pose, and so on. Most of the post-processing was done with
Basalt
[calibration](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Calibration.md?ref_type=heads#camera-imu-mocap-calibration)
and
[alignment](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Realsense.md?ref_type=heads#generating-time-aligned-ground-truth)
tools, as well as the
[xrtslam-metrics](https://gitlab.freedesktop.org/mateosss/xrtslam-metrics)
scripts for Monado tracking. The post-processing process is documented in [this
video][post-processing-video] which goes through making the [MIPB08] dataset ready
for use starting from its raw version.
### Data
#### Camera samples
In the `vive` driver from Monado, we don't have direct access to the camera
device timestamps but only to V4L2 timestamps. These are not exactly hardware
timestamps and have some offset with respect to the device clock in which the
IMU samples are timestamped.
The camera frames can be found in the `camX/data` directory as PNG files with
names corresponding to their V4L2 timestamps. The `camX/data.csv` file contains
aligned timestamps of each frame. The `camX/data.extra.csv` also contains the
original V4L2 timestamp and the "host timestamp" which is the time at which the
host computer had the frame ready to use after USB transmission. By separating
arrival time and exposure time algorithms can be made to be more robust for
real time operation.
The cameras of the Valve Index have global shutters with a resolution of 960×960
streaming at 54fps. They have auto exposure enabled. While the cameras of the
Index are RGB you will find only grayscale images in these datasets. The
original images are provided in YUYV422 format but only the luma component is
stored.
For each dataset, the camera timestamps are aligned with respect to IMU
timestamps by running visual-only odometry with Basalt on a 30-second subset of
the dataset. The resulting trajectory is then aligned with the
[`basalt_time_alignment`](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Realsense.md?ref_type=heads#generating-time-aligned-ground-truth)
tool that aligns the rotational velocities of the trajectory with the gyroscope
samples and returns the resulting offset in nanoseconds. That correction is then
applied to the dataset. Refer to the post-processing walkthrough
[video][post-processing-video] for more details.
#### IMU samples
The IMU timestamps are device timestamps, they come at about 1000Hz. We provide
an `imu0/data.raw.csv` file that contains the raw measurements without any axis
scale misalignment o bias correction. `imu0/data.csv` has the
scale misalignment and bias corrections applied so that the SLAM system can
ignore those corrections. `imu0/data.extra.csv` contains the arrival time of the
IMU sample to the host computer for algorithms that want to adapt themselves to
work in real time.
#### Ground truth information
The ground truth setup consists of three lighthouses 2.0 base stations and a
SteamVR session providing tracking data through the OpenVR API to Monado. While
not as precise as other MoCap tracking systems like OptiTrack or Vicon it
should still provide pretty good accuracy and precision close to the 1mm range.
There are different attempts at studying the accuracy of SteamVR tracking that
you can check out like
[this](https://dl.acm.org/doi/pdf/10.1145/3463914.3463921),
[this](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956487/pdf/sensors-21-01622.pdf),
or [this](http://doc-ok.org/?p=1478). When a tracking system gets closer to
millimeter accuracy these datasets will no longer be as useful for improving it.
The raw ground truth data is stored in `gt/data.raw.csv`. OpenVR does not provide
timestamps and as such, the timestamps recorded are from when the host asks
OpenVR for the latest pose with a call to
[`GetDeviceToAbsoluteTrackingPose`](https://github.com/ValveSoftware/openvr/wiki/IVRSystem::GetDeviceToAbsoluteTrackingPose).
The poses contained in this file are not of the IMU but of the headset origin as
interpreted by SteamVR, which usually is between the middle of the eyes and
facing towards the displays. The file `gt/data.csv` corrects each entry of the
previous file with timestamps aligned with the IMU clock and poses of the IMU
instead of this headset origin.
#### Calibration
There are multiple calibration datasets in the
[`MIC_calibration`](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIC_calibration)
directory. There are camera-focused and IMU-focused calibration datasets. See
the
[README.md](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIC_calibration/README.md)
file in there for more information on what each sequence is.
In the
[`MI_valve_index/extras`](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/extras)
directory you can find the following files:
- [`calibration.json`](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/extras/calibration.json):
Calibration file produced with the
[`basalt_calibrate_imu`](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Calibration.md?ref_type=heads#camera-imu-mocap-calibration)
tool from
[`MIC01_camcalib1`](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIC_calibration/MIC01_camcalib1.zip)
and
[`MIC04_imucalib1`](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIC_calibration/MIC04_imucalib1.zip)
datasets with camera-IMU time offset and IMU bias/misalignment info removed so
that it works with the fully the all the datasets by default which are fully
post-processed and don't require those fields.
- [`calibration.extra.json`](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/extras/calibration.extra.json):
Same as `calibration.json` but with the cam-IMU time offset and IMU bias and
misalignment information filled in.
- [`factory.json`](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/extras/factory.json):
JSON file exposed by the headset's firmware with information of the device. It
includes camera and display calibration as well as more data that might be of
interest. It is not used but included for completeness' sake.
- [`other_calibrations/`](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/extras/other_calibrations):
Calibration results obtained from the other calibration datasets. Shown for
comparison and ensuring that all of them have similar values.
`MICXX_camcalibY` has camera-only calibration produced with the
[`basalt_calibrate`](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Calibration.md?ref_type=heads#camera-calibration)
tool, while the corresponding `MICXX_imucalibY` datasets use these datasets as
a starting point and have the `basalt_calibrate_imu` calibration results.
##### Camera model
By default, the `calibration.json` file provides parameters `k1`, `k2`, `k3`,
and `k4` for the [Kannala-Brandt camera
model](https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1KannalaBrandtCamera4.html#a423a4f1255e9971fe298dc6372345681)
with fish-eye distortion (also known as [OpenCV's
fish-eye](https://docs.opencv.org/3.4/db/d58/group__calib3d__fisheye.html#details)).
Calibrations with other camera models might be added later on, otherwise, you
can use the calibration sequences for custom calibrations.
##### IMU model
For the default `calibration.json` where all parameters are zero, you can ignore
any model and just use the measurements present in `imu0/data.csv` directly. If
instead, you want to use the raw measurements from `imu0/data.raw.csv` you will
need to apply the Basalt
[accelerometer](https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1CalibAccelBias.html#details)
and
[gyroscope](https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1CalibGyroBias.html#details)
models that use a misalignment-scale correction matrix together with a constant
initial bias. The random walk and white noise parameters were not computed and
default reasonable values are used instead.
#### Post-processing walkthrough
If you are interested in understanding the step-by-step procedure of
post-processing of the dataset, below is a video detailing the procedure for the
[MIPB08] dataset.
[](https://www.youtube.com/watch?v=0PX_6PNwrvQ)
### Evaluation
These are the results of running the
[current](https://gitlab.freedesktop.org/mateosss/basalt/-/commits/release-b67fa7a4?ref_type=tags)
Monado tracker that is based on
[Basalt](https://gitlab.com/VladyslavUsenko/basalt) on the dataset sequences.
| Seq. | Avg. time\* | Avg. feature count | ATE (m) | RTE 100ms (m) \*\* | SDM 0.01m (m/m) \*\*\* |
| :------ | :--------------- | :-------------------- | :---------------- | :---------------------- | :--------------------- |
| MIO01 | 10.04 ± 1.43 | [36 23] ± [28 18] | 0.605 ± 0.342 | 0.035671 ± 0.033611 | 0.4246 ± 0.5161 |
| MIO02 | 10.41 ± 1.48 | [32 18] ± [25 16] | 1.182 ± 0.623 | 0.063340 ± 0.059176 | 0.4681 ± 0.4329 |
| MIO03 | 10.24 ± 1.37 | [47 26] ± [26 16] | 0.087 ± 0.033 | 0.006293 ± 0.004259 | 0.2113 ± 0.2649 |
| MIO04 | 9.47 ± 1.08 | [27 16] ± [25 16] | 0.210 ± 0.100 | 0.013121 ± 0.010350 | 0.3086 ± 0.3715 |
| MIO05 | 9.95 ± 1.01 | [66 34] ± [33 21] | 0.040 ± 0.016 | 0.003188 ± 0.002192 | 0.1079 ± 0.1521 |
| MIO06 | 9.65 ± 1.06 | [44 28] ± [33 22] | 0.049 ± 0.019 | 0.010454 ± 0.008578 | 0.2620 ± 0.3684 |
| MIO07 | 9.63 ± 1.16 | [46 26] ± [30 19] | 0.019 ± 0.008 | 0.002442 ± 0.001355 | 0.0738 ± 0.0603 |
| MIO08 | 9.74 ± 0.87 | [29 22] ± [18 16] | 0.059 ± 0.021 | 0.007167 ± 0.004657 | 0.1644 ± 0.3433 |
| MIO09 | 9.94 ± 0.72 | [44 29] ± [14 8] | 0.006 ± 0.003 | 0.002940 ± 0.002024 | 0.0330 ± 0.0069 |
| MIO10 | 9.48 ± 0.82 | [35 21] ± [18 10] | 0.016 ± 0.009 | 0.004623 ± 0.003310 | 0.0620 ± 0.0340 |
| MIO11 | 9.34 ± 0.79 | [32 20] ± [19 10] | 0.024 ± 0.010 | 0.007255 ± 0.004821 | 0.0854 ± 0.0540 |
| MIO12 | 11.05 ± 2.20 | [43 23] ± [31 19] | 0.420 ± 0.160 | 0.005298 ± 0.003603 | 0.1546 ± 0.2641 |
| MIO13 | 10.47 ± 1.89 | [35 21] ± [24 18] | 0.665 ± 0.290 | 0.026294 ± 0.022790 | 1.0180 ± 1.0126 |
| MIO14 | 9.27 ± 1.03 | [49 31] ± [30 21] | 0.072 ± 0.028 | 0.002779 ± 0.002487 | 0.1657 ± 0.2409 |
| MIO15 | 9.75 ± 1.16 | [52 26] ± [29 16] | 0.788 ± 0.399 | 0.011558 ± 0.010541 | 0.6906 ± 0.6876 |
| MIO16 | 9.72 ± 1.26 | [33 17] ± [25 15] | 0.517 ± 0.135 | 0.013268 ± 0.011355 | 0.4397 ± 0.7167 |
| MIPB01 | 10.28 ± 1.25 | [63 46] ± [34 24] | 0.282 ± 0.109 | 0.006797 ± 0.004551 | 0.1401 ± 0.1229 |
| MIPB02 | 9.88 ± 1.08 | [55 37] ± [30 20] | 0.247 ± 0.097 | 0.005065 ± 0.003514 | 0.1358 ± 0.1389 |
| MIPB03 | 10.21 ± 1.12 | [66 44] ± [32 23] | 0.186 ± 0.103 | 0.005938 ± 0.004261 | 0.1978 ± 0.3590 |
| MIPB04 | 9.58 ± 1.02 | [51 37] ± [24 17] | 0.105 ± 0.060 | 0.004822 ± 0.003428 | 0.0652 ± 0.0555 |
| MIPB05 | 9.97 ± 0.97 | [73 48] ± [32 23] | 0.039 ± 0.017 | 0.004426 ± 0.002828 | 0.0826 ± 0.1313 |
| MIPB06 | 9.95 ± 0.85 | [58 35] ± [32 21] | 0.050 ± 0.022 | 0.004164 ± 0.002638 | 0.0549 ± 0.0720 |
| MIPB07 | 10.07 ± 1.00 | [73 47] ± [31 20] | 0.064 ± 0.038 | 0.004984 ± 0.003170 | 0.0785 ± 0.1411 |
| MIPB08 | 9.97 ± 1.08 | [71 47] ± [36 24] | 0.636 ± 0.272 | 0.004066 ± 0.002556 | 0.0740 ± 0.0897 |
| MIPP01 | 10.03 ± 1.21 | [36 22] ± [21 15] | 0.559 ± 0.241 | 0.009227 ± 0.007765 | 0.3472 ± 0.9075 |
| MIPP02 | 10.19 ± 1.20 | [42 22] ± [22 15] | 0.257 ± 0.083 | 0.011046 ± 0.010201 | 0.5014 ± 0.7665 |
| MIPP03 | 10.13 ± 1.24 | [37 20] ± [23 15] | 0.260 ± 0.101 | 0.008636 ± 0.007166 | 0.3205 ± 0.5786 |
| MIPP04 | 9.74 ± 1.09 | [38 23] ± [22 16] | 0.256 ± 0.144 | 0.007847 ± 0.006743 | 0.2586 ± 0.4557 |
| MIPP05 | 9.71 ± 0.84 | [37 24] ± [21 15] | 0.193 ± 0.086 | 0.005606 ± 0.004400 | 0.1670 ± 0.2398 |
| MIPP06 | 9.92 ± 3.11 | [37 21] ± [21 14] | 0.294 ± 0.136 | 0.009794 ± 0.008873 | 0.4016 ± 0.5648 |
| MIPT01 | 10.78 ± 2.06 | [68 44] ± [33 23] | 0.108 ± 0.060 | 0.003995 ± 0.002716 | 0.7109 ± 13.3461 |
| MIPT02 | 10.85 ± 1.27 | [79 54] ± [39 28] | 0.198 ± 0.109 | 0.003709 ± 0.002348 | 0.0839 ± 0.1175 |
| MIPT03 | 10.80 ± 1.55 | [76 52] ± [42 30] | 0.401 ± 0.206 | 0.005623 ± 0.003694 | 0.1363 ± 0.1789 |
| **AVG** | **11.33 ± 1.83** | **[49 23] ± [37 15]** | **0.192 ± 0.090** | **0.009439 ± 0.007998** | **0.3247 ± 0.6130** |
| Seq. | Avg. time\* | Avg. feature count | ATE (m) | RTE 100ms (m) \*\* | SDM 0.01m (m/m) \*\*\* |
| :------ | :--------------- | :-------------------- | :---------------- | :---------------------- | :--------------------- |
| MGO01 | 12.06 ± 2.10 | [19 16] ± [13 12] | 0.680 ± 0.249 | 0.022959 ± 0.019026 | 0.3604 ± 1.3031 |
| MGO02 | 11.20 ± 1.83 | [19 15] ± [19 16] | 0.556 ± 0.241 | 0.027931 ± 0.019074 | 0.3218 ± 0.4599 |
| MGO03 | 9.88 ± 1.92 | [22 16] ± [16 16] | 0.145 ± 0.041 | 0.013003 ± 0.008555 | 0.2433 ± 0.3512 |
| MGO04 | 9.43 ± 1.45 | [16 14] ± [16 16] | 0.261 ± 0.113 | 0.024674 ± 0.017380 | 0.3609 ± 0.4829 |
| MGO05 | 9.93 ± 1.71 | [39 40] ± [17 26] | 0.030 ± 0.011 | 0.004212 ± 0.002632 | 0.0621 ± 0.1044 |
| MGO06 | 10.40 ± 1.84 | [24 22] ± [18 18] | 0.111 ± 0.038 | 0.018013 ± 0.011398 | 0.2496 ± 0.2802 |
| MGO07 | 9.74 ± 1.54 | [30 24] ± [13 12] | 0.021 ± 0.010 | 0.005628 ± 0.003707 | 0.0992 ± 0.1538 |
| MGO08 | 9.42 ± 1.43 | [17 13] ± [11 8] | 0.027 ± 0.015 | 0.013162 ± 0.009729 | 0.1667 ± 0.4068 |
| MGO09 | 10.90 ± 1.70 | [39 34] ± [11 9] | 0.008 ± 0.004 | 0.006278 ± 0.004054 | 0.0738 ± 0.0492 |
| MGO10 | 9.31 ± 1.36 | [29 37] ± [14 17] | 0.008 ± 0.003 | 0.003496 ± 0.002333 | 0.0439 ± 0.0311 |
| MGO11 | 9.26 ± 1.08 | [30 22] ± [13 17] | 0.017 ± 0.006 | 0.006065 ± 0.004285 | 0.0687 ± 0.0604 |
| MGO12 | 9.33 ± 1.39 | [20 19] ± [17 19] | 0.610 ± 0.270 | 0.017372 ± 0.016246 | 0.7225 ± 10.7366 |
| MGO13 | 10.08 ± 1.98 | [18 17] ± [16 17] | 0.683 ± 0.211 | 0.025764 ± 0.017900 | 0.2542 ± 0.3324 |
| MGO14 | 10.00 ± 1.83 | [29 25] ± [17 21] | 0.070 ± 0.025 | 0.012013 ± 0.007674 | 0.1417 ± 0.1850 |
| MGO15 | 9.07 ± 1.39 | [9 7] ± [10 7] | 0.037 ± 0.016 | 0.003737 ± 0.003425 | 0.7053 ± 4.3405 |
| **AVG** | **10.00 ± 1.64** | **[24 21] ± [15 15]** | **0.218 ± 0.084** | **0.013620 ± 0.009828** | **0.2583 ± 1.2852** |
| Seq. | Avg. time\* | Avg. feature count | ATE (m) | RTE 100ms (m) \*\* | SDM 0.01m (m/m) \*\*\* |
| :------ | :-------------- | :-------------------- | :---------------- | :---------------------- | :--------------------- |
| MOO01 | 7.58 ± 1.55 | [30 23] ± [21 20] | 0.281 ± 0.131 | 0.016662 ± 0.010451 | 0.2358 ± 0.3848 |
| MOO02 | 6.89 ± 1.65 | [27 21] ± [24 25] | 0.237 ± 0.101 | 0.015469 ± 0.009201 | 0.1710 ± 0.2281 |
| MOO03 | 7.33 ± 1.77 | [30 26] ± [21 24] | 0.177 ± 0.088 | 0.013521 ± 0.009276 | 0.2610 ± 0.6376 |
| MOO04 | 6.11 ± 1.35 | [22 14] ± [20 16] | 0.065 ± 0.026 | 0.009849 ± 0.005401 | 0.0889 ± 0.1166 |
| MOO05 | 7.04 ± 1.54 | [53 46] ± [20 30] | 0.018 ± 0.007 | 0.003070 ± 0.001838 | 0.0284 ± 0.0181 |
| MOO06 | 6.66 ± 1.58 | [38 35] ± [21 27] | 0.056 ± 0.028 | 0.008395 ± 0.005154 | 0.0847 ± 0.1033 |
| MOO07 | 6.38 ± 1.71 | [43 31] ± [16 21] | 0.013 ± 0.006 | 0.003422 ± 0.002073 | 0.0317 ± 0.0326 |
| MOO08 | 7.17 ± 1.65 | [25 19] ± [19 15] | 0.028 ± 0.015 | 0.011164 ± 0.006958 | 0.0939 ± 0.1051 |
| MOO09 | 8.31 ± 1.84 | [43 38] ± [19 17] | 0.004 ± 0.002 | 0.003284 ± 0.002181 | 0.0063 ± 0.0000 |
| MOO10 | 6.94 ± 1.43 | [38 21] ± [18 15] | 0.010 ± 0.005 | 0.003765 ± 0.002338 | 0.0440 ± 0.0232 |
| MOO11 | 6.66 ± 1.57 | [32 32] ± [18 22] | 0.019 ± 0.010 | 0.005102 ± 0.003253 | 0.0433 ± 0.0356 |
| MOO12 | 5.78 ± 1.40 | [32 34] ± [21 26] | 0.694 ± 0.329 | 0.008292 ± 0.007220 | 0.1275 ± 0.2512 |
| MOO13 | 6.12 ± 1.60 | [21 16] ± [22 19] | 0.501 ± 0.188 | 0.017042 ± 0.010342 | 0.1448 ± 0.1551 |
| MOO14 | 7.07 ± 1.32 | [26 19] ± [17 16] | 0.113 ± 0.058 | 0.007743 ± 0.004316 | 0.1130 ± 0.1661 |
| MOO15 | 6.51 ± 1.70 | [20 11] ± [15 6] | 0.629 ± 0.312 | 0.015308 ± 0.014007 | 0.7254 ± 0.3257 |
| MOO16 | 5.21 ± 1.08 | [23 28] ± [6 8] | 0.046 ± 0.022 | 0.001441 ± 0.001238 | 0.1750 ± 0.1788 |
| **AVG** | **6.74 ± 1.55** | **[31 26] ± [19 19]** | **0.181 ± 0.083** | **0.008971 ± 0.005953** | **0.1484 ± 0.1726** |
- \*: Average frame time. On an AMD Ryzen 7 5800X CPU. Run with pipeline fully
saturated. Real time operation frame times should be slightly lower.
- \*\*: RTE using delta of 6 frames (11ms)
- \*\*\*: The SDM metric is similar to RTE, it represents distance in meters
drifted for each meter of the dataset. The metric is implemented in the
[xrtslam-metrics](https://gitlab.freedesktop.org/mateosss/xrtslam-metrics)
project.
|