Write readme

README.md

@@ -4,7 +4,227 @@ license: cc-by-4.0
 
 # Monado SLAM Datasets
 
-As of now these are permissively licensed datasets for visual-inertial
-egocentric tracking with focus on the XR use-case. They were recorded on a Valve
-Index VR headset with ground truth provided from lighthouse base stations and
-SteamVR. More technical information about the dataset is coming soon.
+The [Monado SLAM datasets
+(MSD)](https://huggingface.co/datasets/collabora/monado-slam-datasets), are
+egocentric visual-inertial SLAM datasets recorded for improving the
+[Basalt](https://gitlab.com/VladyslavUsenko/basalt)-based inside-out tracking
+component of the [Monado](https://monado.dev) project. These have a permissive
+license [CC-BY 4.0](http://creativecommons.org/licenses/by/4.0/), meaning you
+can use them for any purpose you want, including commercial, and only a mention
+of the original project is required. The creation of these datasets was
+supported by [Collabora](https://collabora.com)
+
+Monado is an open-source OpenXR runtime that you can use to make devices OpenXR
+compatible. It also provides drivers for different existing hardware thanks to
+different contributors in the community creating drivers for it. Monado provides
+different XR-related modules that these drivers can use. To be more specific,
+inside-out head tracking is one of those modules and, while you can use
+different tracking systems, the main system is a [fork of
+Basalt](https://gitlab.freedesktop.org/mateosss/basalt). Creating a good
+open-source tracking solution requires a solid measurement pipeline to
+understand how changes in the system affect tracking quality. For this reason,
+the creation of these datasets was essential.
+
+These datasets are very specific to the XR use-case as they contain VI-SLAM
+footage recorded from devices such as VR headsets but other devices like phones
+or AR glasses might be added in the future. These were made since current SLAM
+datasets like EuRoC or TUM-VI were not specific enough for XR, or they didn't
+have permissively enough usage licenses.
+
+For questions or comments you can use the Hugging Face
+[Community](https://huggingface.co/datasets/collabora/monado-slam-datasets/discussions),
+join Monado's discord [server](https://discord.gg/8RkJgRJ) and ask in the
+`#slam` channel, or send an email to <[email protected]>.
+
+# Valve Index datasets
+
+These datasets were recorded using a Valve Index with the `vive` driver in
+Monado and they have groundtruth 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 realtime timestamp alignment techniques.
+
+The dataset is postprocessed to reduce as much as possible special treatment
+from SLAM systems: camera-IMU and groundtruth-IMU timestamp alignment, IMU
+alignment and bias calibration has 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 postprocessing process is documented in [this
+video][postprocessing-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 the their V4L2 timestamps. The `camX/data.csv` file
+contains aligned timestamp 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 are global shutter with a resolution of 960x960
+streaming at 54fps. They have autoexposure 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 postprocessing walkthrough
+[video][postprocessing-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 nor 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 on real-time.
+
+### Groundtruth information
+
+The groundtruth 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 a 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 checkout 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
+milimiter accuracy these datasets will no longer be as useful for improving it.
+
+The raw groundtruth 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
+there](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIC_calibration/README.md)
+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
+  postprocessed 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 exposed by the headset used for recording with information from factory
+  that include calibration and other data. It's not used for anything but might
+  be of interest.
+- [`other_calibrations/`](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/extras/other_calibrations):
+  Results from calibrating using the other datasets for comparisson and checking
+  most of them are similar. `MICXX_camcalibY` have 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 uses 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
+postprocessing of the dataset, below is a video detailing the procedure for the
+[MIPB08] dataset.
+
+[![Post-processing walkthrough video](https://img.youtube.com/vi/0PX_6PNwrvQ/0.jpg)](https://www.youtube.com/watch?v=0PX_6PNwrvQ)
+
+## Sequences
+
+- [MIC_calibration](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIC_calibration):
+  Calibration sequences recording
+  [this](https://drive.google.com/file/d/1DqKWgePodCpAKJCd_Bz-hfiEQOSnn_k0)
+  calibration target from Kalibr with the squares of the target having sides of
+  3cm. Some sequeneces are focused on camera calibration covering the image
+  planes of both stereo cameras while others on IMU calibration properly
+  exciting all six components of the IMU.
+- [MIP_playing](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIO_others): Datasets in which
+  the user is playing a particular VR game on SteamVR while Monado records
+  the datasets.
+  - [MIPB_beat_saber](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber):
+    This contains different songs played at different speeds. The fitbeat song
+    is one that requires a lot of head movement while [MIPB08] is a long 40min
+    dataset with many levels played.
+  - [MIPP_pistol_whip](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPP_pistol_whip):
+    This is a shooting and music game, each dataset is a different level/song.
+  - [MIPT_thrill_of_the_fight](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPT_thrill_of_the_fight):
+    This is a boxing game.
+- [MIO_others](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIO_others): These are other
+  datasets that might be useful, they include playpretend scenarios in which
+  the user supposed to be playing some particular game, then there is some
+  inspection and scanning/mapping of the room, some very short and
+  lightweight datasets for quick testing, and some datasets with a lot of
+  movement around the environment.
+
+## License
+This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a>
+
+[postprocessing-video]: https://youtu.be/0PX_6PNwrvQ
+[MIPB08]: https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber