FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency
Paper • 2603.08433 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 821 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 809 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, trajectory: string
child 6, trajectory_description: string
child 7, deployment_date: timestamp[s]
child 8, session_name: string
child 9, episode_name: string
child 10, window_s: int64
child 11, anchor_unix_us: int64
child 12, gt_origin_utm: list<item: double>
child 0, item: double
child 13, mcap_bytes: int64
child 14, n_static_transforms: int64
child 15, n_basler_frames: int64
child 16, n_zedx_left_frames: int64
child 17, n_zedx_right_frames: int64
child 18, n_robosense_scans: int64
child 19, n_leishen_scans: int64
child 20, n_navtech_frames: int64
child 21, n_audio_left_clips: int64
child 22, n_audio_right_clips: int64
child 23, n_vectornav_samples: int64
child 24, n_xsens_samples: int64
child 25, n_gt_poses: int64
child 26, n_odom_poses: int64
child 27, n_metadata_messages: int64
child 28, has_image: bool
...
: string
workspaces: list<item: null>
child 0, item: null
sample_collection_name: string
sample_fields: list<item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': {'source': Value('string'), 'paper': Value('string'), 'license': Value('string'), 'note': Value('string')}, 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 821 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 809 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, trajectory: string
child 6, trajectory_description: string
child 7, deployment_date: timestamp[s]
child 8, session_name: string
child 9, episode_name: string
child 10, window_s: int64
child 11, anchor_unix_us: int64
child 12, gt_origin_utm: list<item: double>
child 0, item: double
child 13, mcap_bytes: int64
child 14, n_static_transforms: int64
child 15, n_basler_frames: int64
child 16, n_zedx_left_frames: int64
child 17, n_zedx_right_frames: int64
child 18, n_robosense_scans: int64
child 19, n_leishen_scans: int64
child 20, n_navtech_frames: int64
child 21, n_audio_left_clips: int64
child 22, n_audio_right_clips: int64
child 23, n_vectornav_samples: int64
child 24, n_xsens_samples: int64
child 25, n_gt_poses: int64
child 26, n_odom_poses: int64
child 27, n_metadata_messages: int64
child 28, has_image: bool
...
: string
workspaces: list<item: null>
child 0, item: null
sample_collection_name: string
sample_fields: list<item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': {'source': Value('string'), 'paper': Value('string'), 'license': Value('string'), 'note': Value('string')}, 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for fomo-multimodal-sample
This is a FiftyOne dataset with 6 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
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/fomo-multimodal-sample")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
This dataset is a curated, budget-capped FiftyOne multimodal sample built from
the FoMo dataset (Boxan et al. 2026), a year-long
robotic data collection recorded in Forêt Montmorency, a boreal forest 80 km north
of Quebec City, Canada. The original FoMo dataset totals 9.43 TB across 60
sessions (12 seasonal deployments × up to 6 repeated trajectories); this sample
selects one 30-second episode per trajectory type across 6 distinct deployments
(Nov 2024 – Sep 2025, spanning winter/-19°C through summer conditions), each
authored as a single .mcap file combining every raw sensor stream the original
session recorded: stereo + mono camera, two lidars, FMCW radar, two IMUs, stereo
audio, wheel odometry, and PPK-GNSS ground-truth pose. Total sample size is ~19.8 GB
(6 episodes), selected to fit a 30 GB recon budget while maximizing both trajectory
and seasonal diversity.
- Curated by: harpreetsahota (this FiftyOne multimodal conversion and 6-episode sample selection); original sensor data collected by Norlab (Université Laval) and the University of Toronto Robotics Institute
- Funded by: Natural Sciences and Engineering Research Council of Canada (NSERC) and Fonds de recherche du Québec (FRQNT) grant 2023-NOVA-326877 (HUNTER); Canada Foundation for Innovation Fund grant #39709 (PI: E. Thiffault and F. Anctil); weather data from the Adaptable Earth Observation System project (Canada Foundation for Innovation, Government of Quebec, McGill, UQAM)
- Shared by: harpreetsahota
- Language(s): N/A (sensor/robotics telemetry, no natural language content)
- License: CC BY 4.0
Dataset Sources
- Repository: s3://fomo-dataset (original raw data, public/unsigned S3 access); devkit at fomo.norlab.ulaval.ca
- Paper: Boxan et al. (2026). FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency. arXiv:2603.08433
- Demo: fomo.norlab.ulaval.ca
Uses
Direct Use
Multi-sensor visualization and exploration in FiftyOne's multimodal viewer
(Image, 3D, Map, Plot, Logs, and Message tiles) — inspecting synchronized
camera/lidar/radar/IMU/pose data for a single robot-navigation episode without
downloading the full 9.4 TB source dataset; prototyping or testing multimodal
.mcap ingestion pipelines for off-road robot navigation data; qualitative
comparison of seasonal appearance changes (snow depth, lighting, vegetation)
across the 6 included trajectory/deployment pairs.
Out-of-Scope Use
Not suitable for training object detection, segmentation, or classification models — the source FoMo dataset carries zero object/semantic annotations (confirmed by inspecting the full 60-session bucket structure and by keyword search of the source paper text). Not representative of the full dataset's scale or statistical diversity for SLAM/odometry benchmarking — this is a 6-episode, 30-second-per-episode recon sample, not the complete 60-session, multi-minute-per- session dataset; for actual benchmarking, use the full source S3 bucket.
Dataset Structure
This is a multimodal FiftyOne dataset (dataset.media_type == "multimodal")
with 6 samples, each an authored .mcap episode file. There are no
train/val/test splits — every sample is an independent 30-second episode.
Sample fields
| Field | FiftyOne type | Description |
|---|---|---|
filepath |
StringField |
Path to the sample's .mcap episode file |
trajectory |
StringField |
Trajectory name (red, blue, green, magenta, yellow, orange) |
trajectory_description |
StringField |
Human-readable terrain/length description of the trajectory |
deployment_date |
StringField |
Source deployment folder date, YYYY-MM-DD |
session_name |
StringField |
Original FoMo session folder name, <trajectory>_<YYYY-MM-DD-HH-mm> |
episode_name |
StringField |
Local episode identifier used for this sample |
window_s |
IntField |
Trimmed episode duration in seconds (always 30) |
anchor_unix_us |
IntField |
Episode start time, UNIX microseconds (anchored to the first real lidar frame, not the GT log start, which can lag/lead by tens of seconds) |
gt_origin_utm |
ListField |
[x, y, z] absolute UTM-like coordinates of the episode's first ground-truth pose, subtracted out before logging /gt_pose so positions stay small and float-precision-safe |
mcap_bytes |
IntField |
Size of the episode's .mcap file in bytes |
n_static_transforms |
IntField |
Number of static sensor-extrinsic transforms logged to /tf_static (always 9) |
n_basler_frames, n_zedx_left_frames, n_zedx_right_frames |
IntField |
Frame counts for the mono (Basler) and stereo (ZED X) cameras |
n_robosense_scans, n_leishen_scans |
IntField |
Scan counts for the two lidars |
n_navtech_frames |
IntField |
Radar scan count |
n_audio_left_clips, n_audio_right_clips |
IntField |
1-second audio clip counts per microphone |
n_vectornav_samples, n_xsens_samples |
IntField |
IMU sample counts per unit |
n_gt_poses, n_odom_poses |
IntField |
Ground-truth and wheel-odometry pose counts in the window |
n_metadata_messages |
IntField |
Total messages across all /metadata/* numeric telemetry streams |
has_image, has_pointcloud, has_imu, has_gt_pose, has_audio |
BooleanField |
Capability flags derived from the stream counts above, for filtering episodes without opening the .mcap |
changelog |
StringField |
Verbatim contents of the source session's CHANGELOG.md, if present |
MCAP topics (inside each episode)
Each .mcap file contains ~30 topics, all logged with Foxglove-native schemas
(chosen for compatibility with FiftyOne's multimodal viewer's Image/3D/Map/Plot/
Message tiles):
| Topic(s) | Schema | Tile | Notes |
|---|---|---|---|
/basler/image, /zedx_left/image, /zedx_right/image |
foxglove.CompressedImage (png) |
Image | ZED-X images have their alpha channel stripped before re-encoding (it carried no signal — verified near-constant ≈255 with no correlation to luminance) |
/basler/calibration, /zedx_left/calibration, /zedx_right/calibration |
foxglove.CameraCalibration |
(enables reprojection) | Static, logged once from the source calib/*.json |
/robosense/points, /leishen/points |
foxglove.PointCloud |
3D | x,y,z,intensity (float32) + ring (uint16); the per-point absolute timestamp field present in the raw .bin files is dropped (the per-scan log_time already carries scan time) |
/navtech/image_polar |
foxglove.RawImage (mono8) |
Image | Raw polar radar scan, header columns (per-azimuth timestamp/encoder) stripped |
/navtech/image_bev |
foxglove.RawImage (mono8) |
Image | Cartesian bird's-eye-view radar conversion, using the source devkit's own official polar_to_cartesian() formula |
/audio_left/audio, /audio_right/audio |
foxglove.RawAudio (pcm-s16) |
Message | 44.1 kHz mono 1-second clips |
/vectornav/imu, /xsens/imu |
generic JSON | Plot | {wx,wy,wz,ax,ay,az} per sample |
/gt_pose |
foxglove.PoseInFrame |
3D | PPK-GNSS ground-truth pose, frame_id="map", recentered by subtracting the episode's first pose (see gt_origin_utm); orientation is always the identity quaternion (the source PPK pipeline provides no reliable attitude estimate) |
/tf |
foxglove.FrameTransforms (dynamic) |
(enables 3D) | map→base_link, one transform per ground-truth pose — drives correct 3D placement of the moving robot over time |
/tf_static |
foxglove.FrameTransforms (static) |
(enables 3D) | Full sensor-extrinsic tree rooted at base_link (base_link→{footprint, robosense}, robosense→{basler, zedx_left, navtech, leishen}, zedx_left→{zedx_right, vectornav, xsens}) |
/odom |
foxglove.Odometry |
3D + Plot | Wheel odometry (ideal differential-drive model), frame_id="odom" |
/metadata/* (16 streams: battery, current/voltage/velocity ×2, cmd-velocity ×3, camera brightness/exposure/PTP status, barometer, IMU pressure/temperature, weather, snow depth) |
generic JSON | Plot | Numeric telemetry, windowed to the episode's 30 seconds |
Label types and why
There are no object/segmentation/classification labels — the source FoMo
dataset ships none (verified directly, not assumed from the paper's abstract).
The closest thing to a "label" is the ground-truth pose trajectory
(/gt_pose + dynamic /tf), mapped to foxglove.PoseInFrame rather than a
FiftyOne Detection/Classification type because it is inherently a
continuous, time-varying multimodal stream, not a per-sample annotation — this
is also why it lives inside the .mcap rather than as a separate FiftyOne label
field. Note that including ground truth in the .mcap at all is an addition
beyond the source devkit's own official converter, which omits it entirely
(their pipeline treats gt.txt as an offline-evaluation-only artifact).
dataset.info
The dataset-level info dict records the original S3 source, paper citation,
license, and a short note explaining the sampling/authoring approach (30-second
trimmed episodes, 30 GB budget, one per trajectory type across 6 deployments).
Parsing decisions
- Lidar
.binpoint struct is 26 bytes/point, not the paper/tutorial's stated 6×float32 (24 bytes):x,y,z,intensity(float32) +ring(uint16) +timestamp(uint64, µs) — discovered empirically by byte-stride search, then independently confirmed against the source devkit's own Python loader. - ZED-X PNGs are RGBA, not RGB — alpha is near-constant noise, dropped before logging.
- GT/odom coordinates:
gt.txtpositions are absolute UTM-like coordinates; recentered per episode by subtracting the first pose (stored ingt_origin_utmfor reversibility). Wheel odometry is already episode-relative, so it is logged as-is. odom.csvhas a duplicated, broken header (tax,tay,tazappears twice; the second trio is always empty) — only the first 14 real columns are parsed.- Radar (
navtech/*.png): decoded per the Oxford radar-dataset convention (8-byte per-azimuth timestamp + 2-byte encoder + 1 reserved byte + range bins), verified against the source devkit; both the raw polar form and a Cartesian BEV conversion (using the devkit's exact official formula) are included. - Metadata CSVs (
meteo_data.csv,snow_data.csv) have a 3-row header (name/unit/stat-type) — the two extra rows are skipped when parsing. - Episode window anchoring: each 30-second window starts at the first real lidar frame timestamp, not the GT log start — in one deployment the GT logger started up to 56 seconds before the sensors, which would otherwise produce an empty window.
- Episode/session selection: from the 60 available sessions, 6 were chosen to maximize both trajectory-type coverage (all 6: red, blue, green, magenta, yellow, orange) and seasonal/deployment diversity (6 distinct dates spanning Nov 2024–Sep 2025), while keeping the total raw+authored footprint under a 30 GB budget.
Dataset Creation
Curation Rationale
The source FoMo dataset (9.43 TB, 60 sessions) is far too large to explore
directly, and its raw per-file format (individual PNG/.bin/.csv files per
sensor per timestamp) is not natively viewable in FiftyOne. This sample exists to
(a) demonstrate that FoMo's raw sensor format can be authored into FiftyOne's
.mcap-based multimodal viewer, and (b) provide a small, diverse, quickly
downloadable sample — one episode per trajectory type across 6 distinct seasonal
deployments — for exploring the dataset's structure and multi-season character
without downloading the full source dataset.
Source Data
Data Collection and Processing
The original FoMo data was collected using a Clearpath Warthog uncrewed ground vehicle equipped with two lidars (RoboSense Ruby Plus, Leishen LS128S1), an FMCW radar (Navtech CIR-304H), a stereo camera (ZED X) and a monocular camera (Basler ace2), two IMUs (VectorNav VN100, Xsens MTi-30), two microphones, and three rover plus one static GNSS receiver for PPK ground-truth generation. Recording took place across 12 deployments between November 2024 and October 2025 in Forêt Montmorency, spanning conditions from -19°C with over 1 m of snow to 18°C summer days. Ground truth was generated by fusing the three rover GNSS trajectories via a point-to-Gaussian optimization against a CORS-corrected static base station (see the source paper's "Ground Truth" section for the full pipeline).
For this FiftyOne sample specifically: 6 of the 60 sessions were selected (see
Parsing decisions above), each trimmed to its first 30 seconds of synchronized
sensor data, then authored into a single .mcap file per episode using
foxglove-sdk. Every raw stream was parsed directly from source bytes (not
assumed from the paper or tutorial documentation) and cross-checked against the
source devkit's own Python/Rust loaders where available.
Who are the source data producers?
The original sensor data was collected by researchers at Norlab (Université Laval) and the University of Toronto Robotics Institute, operating the UGV during field deployments in Forêt Montmorency, Quebec, Canada.
Annotations
Annotation process
None. The source FoMo dataset contains no manual or model-generated
object/segmentation/classification annotations of any kind. The only
ground-truth signal is the PPK-GNSS-derived robot pose trajectory
(gt.txt/gt_covariance.csv), produced via automated post-processing of the
recorded GNSS receiver data (RINEX + PPK in Emlid Studio, followed by a
point-to-Gaussian multi-receiver fusion), included in this sample as the
/gt_pose and /tf (map→base_link) streams in each episode's .mcap.
Who are the annotators?
Not applicable — no manual annotation was performed. The ground-truth pose trajectory was generated automatically by the original FoMo authors' PPK-GNSS post-processing pipeline.
Personal and Sensitive Information
Per the source paper, camera images were manually anonymized for faces and license plates using a custom-built pipeline before public release. No other personal or sensitive information is present — the recordings are sensor telemetry from an unpopulated boreal forest and gravel/paved access roads.
Citation
BibTeX:
@misc{Boxan2026_fomo,
title = {{FoMo: A Multi-Season Dataset for Robot Navigation in For\^et Montmorency}},
author = {Matěj Boxan and Gabriel Jeanson and Alexander Krawciw and Effie Daum
and Xinyuan Qiao and Sven Lilge and Timothy D. Barfoot and François Pomerleau},
year = {2026},
eprint = {2603.08433},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2603.08433}
}
APA:
Boxan, M., Jeanson, G., Krawciw, A., Daum, E., Qiao, X., Lilge, S., Barfoot, T. D., & Pomerleau, F. (2026). FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency. arXiv:2603.08433.
More Information
This is a 6-of-60-session, 30-seconds-per-session sample of the full FoMo
dataset, selected under a 30 GB size budget — it is not a substitute for the full
9.43 TB source dataset for benchmarking purposes. The full dataset (12
deployments, 60 sessions, six trajectory types, one year of seasonal coverage)
is publicly available, unsigned, at s3://fomo-dataset.
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