Fix README typos

README.md

@@ -8,7 +8,7 @@ license: cc-by-4.0
 
 The [Monado SLAM datasets
 (MSD)](https://huggingface.co/datasets/collabora/monado-slam-datasets), are
-egocentric visual-inertial SLAM datasets recorded for improving the
+egocentric visual-inertial SLAM datasets recorded to improve 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
@@ -27,13 +27,13 @@ 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
+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
+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 <mateo.demayo@collabora.com>.
@@ -76,7 +76,7 @@ join Monado's discord [server](https://discord.gg/8RkJgRJ) and ask in the
     - [MIO15_moving_person_props](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIO_others/MIO15_moving_person_props.zip): <details style="display: inline;cursor: pointer;user-select: none"><summary>Preview 5x</summary><video width="320" height="320" controls preload="none"><source src="https://huggingface.co/datasets/collabora/monado-slam-datasets/resolve/main/M_monado_datasets/MI_valve_index/extras/previews/MIO15_moving_person_props.webm" type="video/webm"/>Video tag not supported.</video></details>
     - [MIO16_moving_screens_person_props](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIO_others/MIO16_moving_screens_person_props.zip): <details style="display: inline;cursor: pointer;user-select: none"><summary>Preview 5x</summary><video width="320" height="320" controls preload="none"><source src="https://huggingface.co/datasets/collabora/monado-slam-datasets/resolve/main/M_monado_datasets/MI_valve_index/extras/previews/MIO16_moving_screens_person_props.webm" type="video/webm"/>Video tag not supported.</video></details>
   - [MIP_playing](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing)
-    - [MIPB_beat_sabet](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber)
+    - [MIPB_beat_saber](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber)
       - [MIPB01_beatsaber_100bills_360_normal](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber/MIPB01_beatsaber_100bills_360_normal.zip): <details style="display: inline;cursor: pointer;user-select: none"><summary>Preview 5x</summary><video width="320" height="320" controls preload="none"><source src="https://huggingface.co/datasets/collabora/monado-slam-datasets/resolve/main/M_monado_datasets/MI_valve_index/extras/previews/MIPB01_beatsaber_100bills_360_normal.webm" type="video/webm"/>Video tag not supported.</video></details>
       - [MIPB02_beatsaber_crabrave_360_hard](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber/MIPB02_beatsaber_crabrave_360_hard.zip): <details style="display: inline;cursor: pointer;user-select: none"><summary>Preview 5x</summary><video width="320" height="320" controls preload="none"><source src="https://huggingface.co/datasets/collabora/monado-slam-datasets/resolve/main/M_monado_datasets/MI_valve_index/extras/previews/MIPB02_beatsaber_crabrave_360_hard.webm" type="video/webm"/>Video tag not supported.</video></details>
       - [MIPB03_beatsaber_countryrounds_360_expert](https://huggingface.co/datasets/collabora/monado-slam-datasets/blob/main/M_monado_datasets/MI_valve_index/MIP_playing/MIPB_beat_saber/MIPB03_beatsaber_countryrounds_360_expert.zip): <details style="display: inline;cursor: pointer;user-select: none"><summary>Preview 5x</summary><video width="320" height="320" controls preload="none"><source src="https://huggingface.co/datasets/collabora/monado-slam-datasets/resolve/main/M_monado_datasets/MI_valve_index/extras/previews/MIPB03_beatsaber_countryrounds_360_expert.webm" type="video/webm"/>Video tag not supported.</video></details>
@@ -100,48 +100,48 @@ join Monado's discord [server](https://discord.gg/8RkJgRJ) and ask in the
 ## 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
+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 realtime timestamp alignment techniques.
+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 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
+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 postprocessing process is documented in [this
-video][postprocessing-video] which goes through making the [MIPB08] dataset ready
+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
+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
+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.
@@ -152,33 +152,33 @@ 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.
+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 nor bias correction. `imu0/data.csv` has the
-scale-misalignment and bias corrections applied so that the SLAM system can
+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 on real-time.
+work in real time.
 
-#### Groundtruth information
+#### Ground truth information
 
-The groundtruth setup consists of three lighthouses 2.0 base stations and a
+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 a other MoCap tracking systems like OptiTrack or Vicon it
+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 checkout like
+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
-milimiter accuracy these datasets will no longer be as useful for improving it.
+millimeter 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
+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).
@@ -193,11 +193,13 @@ instead of this headset origin.
 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.
+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:
+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
@@ -208,18 +210,18 @@ In the [`MI_valve_index/extras`](https://huggingface.co/datasets/collabora/monad
   [`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.
+  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 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.
+  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):
-  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
+  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.
@@ -232,26 +234,26 @@ model](https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1KannalaBr
 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.
+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
+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
+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
+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
-postprocessing of the dataset, below is a video detailing the procedure for the
+post-processing 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)
@@ -259,15 +261,15 @@ postprocessing of the dataset, below is a video detailing the procedure for the
 ### Sequences
 
 - [MIC_calibration](https://huggingface.co/datasets/collabora/monado-slam-datasets/tree/main/M_monado_datasets/MI_valve_index/MIC_calibration):
-  Calibration sequences recording
+  Calibration sequences that record
   [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
+  3 cm. Some sequences 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.
+- [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
@@ -276,17 +278,17 @@ postprocessing of the dataset, below is a video detailing the procedure for the
     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.
+- [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 play-pretend
+  scenarios in which the user is 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
+[post-processing-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