#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for Boreas Multimodal (MCAP)
A FiftyOne build of **Boreas** and **Boreas Road Trip (Boreas-RT)**, the
multi-season and multi-route autonomous driving datasets from the
Autonomous Space Robotics Laboratory (ASRL) at UTIAS. This build repackages
3 driving sequences and 6 object-detection windows as time-synchronized
[MCAP](https://mcap.dev/) recordings for FiftyOne’s native
[multimodal dataset support](https://docs.voxel51.com/user_guide/multimodal.html)
(FiftyOne 1.19+). Each sample is one episode, viewable in FiftyOne’s tiled
multimodal viewer with synchronized camera, 128-beam lidar point cloud,
decoded 360° radar imagery, GPS/map track, and IMU/encoder/velocity
telemetry — plus, for the object-detection episodes, 3D bounding boxes
rendered natively in the 3D tile and as 2D wireframe overlays on the camera
and radar images.
Boreas/Boreas-RT are raw multi-sensor datasets for benchmarking
odometry/localization algorithms across seasons, weather, and road types;
only one recording (`boreas-objects-v1`) carries 3D object annotations
(Car/Pedestrian/Cyclist/Misc). This repackaging does not add or alter any
ground truth; see [Dataset Creation](#dataset-creation) for exactly what
was kept, converted, and left out.
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8 samples.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/boreas-multimodal")
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
Boreas is a multi-season autonomous driving dataset collected by repeatedly
driving a route near the University of Toronto over one full year (Nov
2020-Nov 2021), capturing sun, rain, falling snow, and every season in
between — 44 public sequences, 350 km total. Boreas Road Trip (Boreas-RT)
extends this with 9 new, more diverse routes (suburbs, industrial, urban,
forest, farm, tunnel, skyway, regional, freeway), each repeated on average
6 times — 60 sequences, 643 km total — adding an Aeva Aeries II FMCW
Doppler lidar (32 of 60 sequences) and a standalone Silicon Sensing DMU41
IMU on top of the original sensor suite (128-beam Velodyne Alpha-Prime
lidar, 360° Navtech scanning radar, 5MP FLIR Blackfly S camera, Applanix
POS LV GNSS-INS). A separate, dedicated recording (`boreas-objects-v1`,
~104 minutes, sunny weather only) provides 326,180 3D object-detection
annotations (Car/Pedestrian/Cyclist/Misc) across 7,111 labeled lidar
frames. This FiftyOne build covers 2 driving-sequence episodes (one
original Boreas, one Boreas-RT) and 6 object-detection windows carved from
`boreas-objects-v1` (see [Curation Rationale](#curation-rationale)).
- **Curated by:** Autonomous Space Robotics Laboratory (ASRL), University
of Toronto Institute for Aerospace Studies (UTIAS), in collaboration with
Applanix/Trimble — original Boreas/Boreas-RT data collection, sensor
calibration, and ground-truth pose post-processing. 3D object annotations
for `boreas-objects-v1` were produced by the Scale.ai data annotation
service. This MCAP/FiftyOne multimodal repackaging (episode authoring,
dataset card) was prepared independently by Harpreet Sahota.
- **Funded by:** Natural Sciences and Engineering Research Council of
Canada (NSERC); Applanix/Trimble (co-authorship and sensor support);
General Motors (vehicle donation); hosting via the Amazon Open Data
Sponsorship Program.
- **Shared by:** Harpreet Sahota (this repackaging); the original Boreas
and Boreas-RT datasets are shared by ASRL via the [AWS Open Data
Registry](https://registry.opendata.aws/boreas/) (`s3://boreas`) and
https://www.boreas.utias.utoronto.ca.
- **Language(s):** N/A (sensor data — camera, lidar, radar, GPS, IMU; no
text).
- **License:** Unknown — the AWS Open Data Registry listing’s License field
is blank, and no license is stated in the `pyboreas` devkit or bucket
contents at the time of this card; see [Citation](#citation) for the
registry’s suggested access citation.
### Dataset Sources
- **Repository (devkit):** https://github.com/utiasASRL/pyboreas
- **Paper:** Burnett, K., Yoon, D. J., Wu, Y., Li, A. Z., Zhang, H., Lu, S.,
Qian, J., Tseng, W.-K., Lambert, A., Leung, K. Y. K., Schoellig, A. P., &
Barfoot, T. D. (2023). *Boreas: A Multi-Season Autonomous Driving
Dataset*. The International Journal of Robotics Research, 42(1-2), 33-42.
arXiv:[2203.10168](https://arxiv.org/abs/2203.10168); Lisus, D., Papais,
K. M., Le Gentil, C., Preston-Krebs, E., Lambert, A., Leung, K. Y. K., &
Barfoot, T. D. (2026). *Boreas Road Trip: A Multi-Sensor Autonomous
Driving Dataset on Challenging Roads*.
arXiv:[2602.16870](https://arxiv.org/abs/2602.16870)
- **Demo:** https://www.boreas.utias.utoronto.ca (official docs/download/
leaderboard site) and https://registry.opendata.aws/boreas/ (AWS Open
Data listing)
## Uses
### Direct Use
- Exercising/demoing FiftyOne’s multimodal MCAP support: synchronized
playback of camera, 128-beam lidar point cloud, decoded 360° radar
imagery, GPS/map track, and IMU/encoder/velocity telemetry across real
autonomous-driving recordings.
- Inspecting 3D object-detection ground truth (Car/Pedestrian/Cyclist
boxes) rendered natively as `foxglove.SceneUpdate` cuboids in the 3D
tile, plus 2D wireframe overlays on the camera and radar bird’s-eye-view
images (both carrying label/uuid/numPoints metadata, inspectable via
FiftyOne’s built-in click-to-inspect panel), across 6 diverse time
windows spread through the ~104-minute `boreas-objects-v1` recording.
- Prototyping odometry/localization analyses against dense ENU-frame
ground-truth pose (`/tf`, `/odom`, `/gps`, `/velocity`) at each sensor’s
native timestamp.
- Comparing sensor suites between the original Boreas platform and the
Boreas-RT extension (adds an Aeva FMCW Doppler lidar and a standalone
DMU41 IMU).
### Out-of-Scope Use
- Reproducing the official odometry/localization/detection leaderboards at
boreas.utias.utoronto.ca — those benchmarks run over full, un-windowed
sequences (44 Boreas + 60 Boreas-RT sequences, each 90-150+ GB raw); this
build covers only 2 driving sequences (each a 10-second window, not the
full sequence) plus 6 short windows carved from the single
`boreas-objects-v1` recording.
- Training or evaluating on `obj_test`-split windows for detection — ground
truth for that split is withheld by the source dataset for leaderboard
fairness (confirmed directly: every `obj_test` window checked has zero
label files); only `obj_train` windows are included here.
- Any task requiring depth maps, semantic/instance segmentation, or an HD
map layer — none exist anywhere in the source dataset (the devkit’s own
`BoreasDataset.map` attribute is an unimplemented `TODO`).
## Dataset Structure
This is a flat (ungrouped) FiftyOne dataset with `media_type: "multimodal"`
and **8 samples**. Each sample is one **episode**, stored as one `.mcap`
file; FiftyOne infers the multimodal media type automatically from the
`.mcap` extension. There are no separate per-frame image or point-cloud
samples — the episode is the sample unit, and every stream inside it
(camera, lidar, radar, GPS, IMU, encoder, and — where present — 3D box
annotations) is decoded live by FiftyOne’s multimodal viewer.
The dataset carries no per-sample tags and `dataset.info` is empty (no
extra dataset-level metadata beyond the per-sample fields below).
### Episodes in this dataset
| `sequence_id` | `episode_tag` | `dataset_variant` | `episode_type` | Duration | `has_boxes` | `has_aeva` | `has_gps` |
|---------------------------|-------------------|---------------------|------------------|------------|---------------|--------------|-------------|
| `boreas-2021-09-02-11-42` | `ep000` | Boreas | odometry | 10.0s | False | False | True |
| `boreas-2024-12-03-12-54` | `ep000` | Boreas-RT | odometry | 10.0s | False | True | True |
| `boreas-objects-v1` | `ep000_objsample` | Boreas | object_detection | 19.5s | True | False | False |
| `boreas-objects-v1` | `ep001_objtrain` | Boreas | object_detection | 19.5s | True | False | False |
| `boreas-objects-v1` | `ep002_objtrain` | Boreas | object_detection | 39.6s | True | False | False |
| `boreas-objects-v1` | `ep003_objtrain` | Boreas | object_detection | 45.7s | True | False | False |
| `boreas-objects-v1` | `ep004_objtrain` | Boreas | object_detection | 19.5s | True | False | False |
| `boreas-objects-v1` | `ep005_objtrain` | Boreas | object_detection | 19.7s | True | False | False |
The 2 driving episodes are fixed 10-second windows starting mid-sequence
(not full sequences — see [Curation Rationale](#curation-rationale)). The 6
`boreas-objects-v1` episodes are 5 `obj_train` windows plus the devkit’s own
`obj_sample` window, spread across the full ~104-minute recording for
scene diversity; `ep000_objsample`’s window is also one of the `obj_train`
windows. `boreas-objects-v1` has no `applanix/imu.csv`/`dmi.csv`/lat-lon
columns at all (an older, 2020-era recording — see [Parsing
decisions](#parsing-decisions)), hence `has_gps` is `False` for all 6.
### Fields
| Field | FiftyOne type | Description |
|-------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
| `filepath` | `StringField` | Absolute path to the episode’s `.mcap` file — the sample’s multimodal media |
| `sequence_id` | `StringField` | Raw Boreas/Boreas-RT sequence ID (`boreas-YYYY-MM-DD-HH-MM`, or `boreas-objects-v1`), verbatim from the S3 folder name |
| `episode_tag` | `StringField` | Local identifier disambiguating multiple episodes from the same `sequence_id` (e.g. the 6 different windows into `boreas-objects-v1`) |
| `episode_type` | `StringField` | `"odometry"` or `"object_detection"` |
| `dataset_variant` | `StringField` | `"Boreas"` or `"Boreas-RT"` |
| `duration_s` | `FloatField` | Episode duration in seconds, computed from the MCAP’s message-time span |
| `message_count` | `IntField` | Total MCAP message count across all channels in the episode |
| `channel_count` | `IntField` | Total MCAP channel (topic) count in the episode (10 for driving/detection episodes, 13 for the Boreas-RT episode) |
| `topics` | `ListField(StringField)` | Every MCAP topic present (see [MCAP topics](#mcap-topics-inside-each-episode) below) |
| `schemas` | `ListField(StringField)` | Every distinct Foxglove schema present in the episode |
| `has_image` | `BooleanField` | Whether the episode has an Image-tile-decodable stream (`foxglove.CompressedImage` or `foxglove.RawImage`) — `True` for every episode |
| `has_pointcloud` | `BooleanField` | Whether it has a decodable point-cloud stream for the 3D tile (`foxglove.PointCloud`) — `True` for every episode |
| `has_gps` | `BooleanField` | Whether it has a decodable GPS fix stream for the Map tile (`foxglove.LocationFix`) — `True` only for the 2 driving episodes |
| `has_boxes` | `BooleanField` | Whether it has a `foxglove.SceneUpdate` (3D box) stream — `True` only for the 6 `boreas-objects-v1` episodes |
| `has_aeva` | `BooleanField` | Whether it has an Aeva FMCW lidar point-cloud stream (`/aeva`) — `True` only for the Boreas-RT episode |
| `has_dmu_imu` | `BooleanField` | Whether it has the standalone DMU41 IMU stream (`/dmu_imu`) — `True` only for the Boreas-RT episode |
| `has_aeva_imu` | `BooleanField` | Whether it has the Aeva built-in IMU stream (`/aeva_imu`) — `True` only for the Boreas-RT episode |
Standard FiftyOne bookkeeping fields (`id`, `metadata`, `created_at`,
`last_modified_at`) are also present but not source-specific.
### MCAP topics (inside each episode)
| Topic(s) | Schema | Tile | Notes |
|------------------------------------------------------------------|----------------------------------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `/camera` | `foxglove.CompressedImage` (png) | Image | Rectified, radially-undistorted, and pre-anonymized (faces/plates blurred) by the original dataset |
| `/camera/calibration` | `foxglove.CameraCalibration` | (enables reprojection) | Static, logged once from `P_camera.txt` + real lens distortion from `camera0_intrinsics.yaml` (when present) |
| `/lidar` | `foxglove.PointCloud` | 3D | Velodyne Alpha-Prime, `x,y,z,intensity` (float32) |
| `/aeva` (Boreas-RT episode only) | `foxglove.PointCloud` | 3D | Aeva Aeries II FMCW lidar, `x,y,z,intensity,radial_velocity` (float32) |
| `/radar` | `foxglove.RawImage` (mono8) | Image | Decoded polar→Cartesian bird’s-eye-view conversion (no native polar-sensor MCAP schema exists); Boreas-RT radar additionally gets Doppler/offset correction before conversion |
| `/tf` | `foxglove.FrameTransform` | (enables 3D) | `enu_ref`→{`camera`,`lidar`,`radar`,`aeva`} per-sensor pose, at each sensor’s own native timestamp (sensors are not hardware-synchronized) |
| `/odom` | `foxglove.Odometry` | 3D + Plot | Lidar-frame pose + ENU/body-frame velocity |
| `/velocity` | generic JSON | Plot | Same velocity/body-rate values as `/odom`, as a flat dict for convenient Plot-tile inspection |
| `/gps` (driving episodes only) | `foxglove.LocationFix` | Map | Lat/lon/heading, converted from the source CSV’s radians to the degrees `LocationFix` expects (see [Parsing decisions](#parsing-decisions)) |
| `/imu`, `/encoder` (driving episodes only) | generic JSON | Plot | Applanix-integrated IMU and wheel-encoder pulse count |
| `/dmu_imu`, `/aeva_imu` (Boreas-RT episode only) | generic JSON | Plot | Standalone DMU41 IMU and Aeva’s built-in IMU |
| `/boxes` (object-detection episodes only) | `foxglove.SceneUpdate` | 3D | One `SceneEntity` per box (id = source `uuid`), `CubePrimitive` sized `[length,width,height]`, colored by class, `metadata` carrying `label`/`type`/`uuid`/`numPoints` |
| `/camera/boxes`, `/radar/boxes` (object-detection episodes only) | `foxglove.ImageAnnotations` | Image | 2D wireframe (camera) / bird’s-eye-view footprint (radar) overlays of the same boxes, each carrying the same `label`/`type`/`uuid`/`numPoints` metadata |
### Label types and why
**No FiftyOne sample-level label fields (`Detections`, `Detections3D`,
etc.) are attached.** Because each sample is a multi-second-to-minute
continuous recording rather than a single frame, there is no
single fixed-length list a sample-level label field could hold. Instead,
the 3D object annotations for the 6 `boreas-objects-v1` episodes are
embedded as additional MCAP topics inside the same timeline as the sensor
data (`/boxes`, `/camera/boxes`, `/radar/boxes` — see [MCAP
topics](#mcap-topics-inside-each-episode)), decoded live by FiftyOne’s
multimodal viewer alongside the camera/lidar/radar tiles, exactly like the
sensor topics themselves. The `has_boxes` sample field exists so episodes
can be filtered without opening every MCAP file first, e.g.
`dataset.match(F("has_boxes"))`.
### Parsing decisions
- **One sample = one episode.** Full Boreas/Boreas-RT sequences are 90-150+
GB raw each (44+60 = 104 sequences total), so only 2 driving episodes
(one original Boreas, one Boreas-RT) were authored, each a fixed
10-second window starting mid-sequence rather than the full recording.
- **Object-detection episode selection.** `boreas-objects-v1` is one
continuous ~104-minute recording; the source dataset’s own detection
benchmark carves it into 53 short (17.6-71.9s) named windows (`obj_train`:
37, `obj_test`: 16) rather than splitting by whole sequence. 6 windows
were selected from `obj_train` (spread across the full recording for
scene diversity) — `obj_test` windows were excluded because their ground
truth is withheld by the source dataset for leaderboard fairness
(confirmed directly: every `obj_test` window checked has zero
`labels_detection/*.txt` files in range, while every `obj_train` window
checked has labels).
- **Boxes with `numPoints == 0` are dropped**, matching the source devkit’s
own `object_detection.ipynb` tutorial (`BoundingBoxes.filter_empty()`),
which always removes boxes with no supporting lidar returns before
display. Had no effect on the specific windows included here (0 of 4,896
boxes checked have `numPoints == 0`), but the authoring script is
defensive against it for other windows.
- **3D boxes are logged at every lidar frame (10 Hz), not just directly
labeled ones (5 Hz)** — the source only labels every other lidar frame
directly; boxes on the in-between frames are produced via the devkit’s
own SO(3) interpolation (`BoundingBoxes.interpolate()`, needs the
`asrl-pylgmath` package), the same codepath the devkit itself uses for
camera/radar frames (which never land on a directly-labeled timestamp).
This is a deliberate scope choice beyond the devkit’s own tutorial, which
only ever shows directly-labeled lidar frames.
- **Box dimension order is `[length, width, height]`** (verified directly
in the source paper’s text: *“(dx,dy,dz) represent the bounding box
dimensions (length, width, and height)”*) — the `readme.txt` shipped
alongside the label files in the S3 bucket incorrectly states the order
as `height, width, length`; this build follows the paper, not the
bucket’s `readme.txt`.
- **`gps_post_process.csv`’s `latitude`/`longitude`/`heading` are in
radians, not degrees** (like the sibling `roll`/`pitch` columns, used
directly with no conversion elsewhere in the devkit) — converted to
degrees for `foxglove.LocationFix.latitude`/`longitude` (which expect
degrees), while `heading` is passed straight through unconverted (it’s
already radians, matching `LocationFix.heading`’s expected units).
- **Angular velocity columns in every pose CSV are stored in `z,y,x`
order**, not `x,y,z` — an intentional, paper-documented convention (“the
flipped ordering… for backward compatibility”), not a bug; reordered to
`x,y,z` before logging.
- **Aeva point fields are `[x,y,z,radial_velocity,intensity,signal_quality, reflectivity,time,point_flags]`** (9 named fields, `point_flags` occupying
2 of the 10 float32 slots as one float64) — this exact order is confirmed
by the Boreas-RT paper’s text, resolving an internal inconsistency in the
devkit itself (a generic per-point-time helper assumes the last column is
always “time”, which is only true for the 6-field Velodyne format, not
the 10-field Aeva format).
- **Radar Doppler/offset correction is applied only to the Boreas-RT
episode**, gated on the devkit’s own `radar_chirp_type_upgrade_time`
constant — applying it to the original (pre-upgrade) Boreas radar would
use a bogus chirp-type value (that sensor generation has no real per-
azimuth chirp data) and corrupt the image.
- **Sensors are not hardware-synchronized** — real per-sensor timestamps
are used as-is for `/tf`, which is correct/expected behavior per both
source papers, not an alignment bug.
## Dataset Creation
### Curation Rationale
Full Boreas/Boreas-RT sequences are far too large for a lightweight
FiftyOne showcase — 104 sequences total (44 Boreas + 60 Boreas-RT), each
90-150+ GB raw, plus a separate 152 GB standalone recording for object
detection. Rather than attempting exhaustive coverage, this build selects:
one original-Boreas driving episode and one Boreas-RT driving episode
(each a 10-second window, chosen to demonstrate the respective sensor
suites — the Boreas-RT one specifically for its Aeva FMCW lidar and dual
IMU streams), plus 6 object-detection windows spread across the full
`boreas-objects-v1` recording for scene diversity, all drawn from the
already-labeled `obj_train` split.
### Source Data
#### Data Collection and Processing
Per ASRL’s published papers and devkit documentation: Boreas was collected
by driving a repeated route (the “Glen Shields” route) near the University
of Toronto over one year (Nov 2020-Nov 2021), captured in every season and
weather condition including falling snow — 44 public sequences, 350 km
total. Sensor suite: a 128-beam Velodyne Alpha-Prime lidar (360°×40° FOV,
~2.2M points/s, 10 Hz), a 360° Navtech CIR304-H scanning radar (0.9°
angular resolution, 0.0596 m/200 m range pre-upgrade or 0.0438 m/250 m
range post-upgrade, 4 Hz), a 5MP FLIR Blackfly S camera (2448×2048, 81°×71°
FOV, 10 Hz, images pre-rectified and pre-anonymized for faces/license
plates), and an Applanix POS LV 220 GNSS-INS (200 Hz, 2-4 cm RTX accuracy)
providing centimetre-level post-processed ground-truth pose in a
fixed ENU frame anchored to the first pose of the first sequence. A
dedicated standalone recording, `boreas-objects-v1` (~104 minutes, sunny
weather), was separately annotated by the Scale.ai data annotation service
with 326,180 3D object boxes (Car/Pedestrian/Cyclist/Misc) across 7,111
labeled lidar frames at 5 Hz.
Boreas Road Trip (Boreas-RT) extends the same collection methodology to 9
new routes across suburban, industrial, urban, rural, and highway
environments (60 sequences, 643 km total), adding an Aeva Aeries II FMCW
Doppler lidar (120°×30° FOV, ~1.0M points/s, 32 of 60 sequences) and a
standalone Silicon Sensing DMU41 IMU (200 Hz) alongside the original sensor
suite, plus Doppler-capable radar firmware (per-azimuth “chirp type”
metadata enabling Doppler velocity extraction and undistortion).
For this repackaging: raw per-frame sensor files, calibration, and
ground-truth CSVs were downloaded from the public, unsigned `s3://boreas`
bucket (targeted time-windowed slices, not full sequences), decoded using
the official `pyboreas` devkit (with `asrl-pylgmath`/`asrl-pysteam` added to
unblock its 3D box interpolation code path, and `foxglove-sdk` for MCAP
authoring), cross-checked byte-for-byte against real downloaded files
rather than trusted from documentation alone, and packed into one `.mcap`
file per episode. No sensor data or ground truth was synthesized,
relabeled, or altered beyond the conversions documented in [Parsing
decisions](#parsing-decisions).
#### Who are the source data producers?
Autonomous Space Robotics Laboratory (ASRL), University of Toronto
Institute for Aerospace Studies (UTIAS), in collaboration with
Applanix/Trimble — original Boreas/Boreas-RT data collection, sensor
calibration, and ground-truth pose post-processing.
### Annotations
#### Annotation process
3D bounding boxes for `boreas-objects-v1` were produced by the Scale.ai
data annotation service, per the original Boreas paper: 7,111 lidar frames
were labeled at 5 Hz (double the labeling rate is achievable via
interpolation, since the lidar itself runs at 10 Hz — see [Parsing
decisions](#parsing-decisions)), yielding 326,180 unique 3D box
annotations across 4 classes (Car, Pedestrian, Cyclist, Misc), divided into
53 continuous scenes (20-70s each) and split into 37 training scenes and 16
test scenes (test-scene labels withheld for the official benchmark). No
other annotation exists anywhere in either dataset — odometry/localization
ground truth is automated GNSS/IMU/wheel-encoder post-processing, not
manual annotation.
#### Who are the annotators?
The Scale.ai data annotation service, for the `boreas-objects-v1` 3D box
annotations only. All other “ground truth” in this dataset (pose,
velocity) is produced automatically by Applanix’s POSPac software suite
from GNSS/IMU/wheel-encoder measurements plus an RTX correction
subscription — no manual annotation is involved.
#### Personal and Sensitive Information
Camera images are pre-anonymized by the original dataset producers: faces
and license plates are blurred using the “Understand.ai Anonymizer” tool
before public release, per the original Boreas paper. This repackaging
performs no additional processing, re-identification, or redaction beyond
what ASRL already released publicly.
## Citation
**BibTeX:**
```bibtex
@article{burnett_ijrr23,
author = {Keenan Burnett and David J Yoon and Yuchen Wu and Andrew Z Li
and Haowei Zhang and Shichen Lu and Jingxing Qian and
Wei-Kang Tseng and Andrew Lambert and Keith YK Leung and
Angela P Schoellig and Timothy D Barfoot},
title = {Boreas: A multi-season autonomous driving dataset},
journal = {The International Journal of Robotics Research},
volume = {42},
number = {1-2},
pages = {33-42},
year = {2023},
doi = {10.1177/02783649231160195}
}
@misc{lisus_brrt26,
author = {Daniil Lisus and Katya M. Papais and Cedric Le Gentil
and Elliot Preston-Krebs and Andrew Lambert and
Keith Y. K. Leung and Timothy D. Barfoot},
title = {Boreas Road Trip: A Multi-Sensor Autonomous Driving
Dataset on Challenging Roads},
year = {2026},
eprint = {2602.16870},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.16870}
}
```
**APA:**
Burnett, K., Yoon, D. J., Wu, Y., Li, A. Z., Zhang, H., Lu, S., Qian, J.,
Tseng, W.-K., Lambert, A., Leung, K. Y. K., Schoellig, A. P., & Barfoot, T.
D. (2023). Boreas: A multi-season autonomous driving dataset. *The
International Journal of Robotics Research*, 42(1-2), 33-42.
Lisus, D., Papais, K. M., Le Gentil, C., Preston-Krebs, E., Lambert, A.,
Leung, K. Y. K., & Barfoot, T. D. (2026). Boreas Road Trip: A Multi-Sensor
Autonomous Driving Dataset on Challenging Roads. arXiv:2602.16870.
> “Boreas Autonomous Driving Dataset was accessed on `DATE` from
> https://registry.opendata.aws/boreas.” — suggested citation text per the
> AWS Open Data Registry listing.
## More Information
This repository is an independently-curated, derived subset of the
official Boreas/Boreas-RT datasets, repackaged as MCAP for FiftyOne’s
multimodal support. It is not an official ASRL/UTIAS artifact. For the
full datasets (all 104 driving sequences, the full `boreas-objects-v1`
recording, the devkit, and the live odometry/localization/detection
leaderboards), see https://www.boreas.utias.utoronto.ca,
https://registry.opendata.aws/boreas/, and
https://github.com/utiasASRL/pyboreas.
## Dataset Card Authors
Harpreet Sahota ([@harpreetsahota](https://huggingface.co/harpreetsahota))
— MCAP repackaging and this card. Original dataset producers are listed
under [Dataset Description](#dataset-description).
## Dataset Card Contact
Harpreet Sahota — https://huggingface.co/harpreetsahota