Datasets:

Voxel51
/

canoe-multimodal

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 418 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 406 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
      child 2, tags: list<item: null>
          child 0, item: null
      child 3, _media_type: string
      child 4, _rand: double
      child 5, sequence_id: string
      child 6, route: string
      child 7, radar_max_range_m: int64
      child 8, duration_s: double
      child 9, message_count: int64
      child 10, channel_count: int64
      child 11, topics: list<item: string>
          child 0, item: string
      child 12, schemas: list<item: string>
          child 0, item: string
      child 13, has_image: bool
      child 14, has_pointcloud: bool
      child 15, has_gps: bool
      child 16, has_imu: bool
      child 17, has_logs: bool
      child 18, source: string
      child 19, _dataset_id: struct<$oid: string>
          child 0, $oid: string
      child 20, created_at: struct<$date: string>
          child 0, $date: string
      child 21, last_modified_at: struct<$date: string>
          child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targets: null
skeletons: null
camera_intrinsics: null
static_transforms: null
anno
...
embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
              child 0, name: string
              child 1, ftype: string
              child 2, embedded_doc_type: null
              child 3, subfield: null
              child 4, fields: list<item: null>
                  child 0, item: null
              child 5, db_field: string
              child 6, description: null
              child 7, info: null
              child 8, read_only: bool
              child 9, created_at: struct<$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, $date: string
media_type: string
frame_fields: list<item: null>
  child 0, item: null
version: string
sample_collection_name: string
last_modified_at: struct<$date: string>
  child 0, $date: string
last_loaded_at: struct<$date: string>
  child 0, $date: string
created_at: struct<$date: string>
  child 0, $date: string
last_deletion_at: null
tags: list<item: null>
  child 0, item: null
active_label_schemas: list<item: null>
  child 0, item: null
persistent: bool
default_classes: list<item: null>
  child 0, item: null
saved_views: list<item: null>
  child 0, item: null
slug: string
to
{'_id': {'$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': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$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:  (... 418 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 406 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, sequence_id: string
                    child 6, route: string
                    child 7, radar_max_range_m: int64
                    child 8, duration_s: double
                    child 9, message_count: int64
                    child 10, channel_count: int64
                    child 11, topics: list<item: string>
                        child 0, item: string
                    child 12, schemas: list<item: string>
                        child 0, item: string
                    child 13, has_image: bool
                    child 14, has_pointcloud: bool
                    child 15, has_gps: bool
                    child 16, has_imu: bool
                    child 17, has_logs: bool
                    child 18, source: string
                    child 19, _dataset_id: struct<$oid: string>
                        child 0, $oid: string
                    child 20, created_at: struct<$date: string>
                        child 0, $date: string
                    child 21, last_modified_at: struct<$date: string>
                        child 0, $date: string
              group_media_types: null
              info: null
              app_config: null
              classes: null
              mask_targets: null
              default_mask_targets: null
              skeletons: null
              camera_intrinsics: null
              static_transforms: null
              anno
              ...
              embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
                            child 0, name: string
                            child 1, ftype: string
                            child 2, embedded_doc_type: null
                            child 3, subfield: null
                            child 4, fields: list<item: null>
                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$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, $date: string
              media_type: string
              frame_fields: list<item: null>
                child 0, item: null
              version: string
              sample_collection_name: string
              last_modified_at: struct<$date: string>
                child 0, $date: string
              last_loaded_at: struct<$date: string>
                child 0, $date: string
              created_at: struct<$date: string>
                child 0, $date: string
              last_deletion_at: null
              tags: list<item: null>
                child 0, item: null
              active_label_schemas: list<item: null>
                child 0, item: null
              persistent: bool
              default_classes: list<item: null>
                child 0, item: null
              saved_views: list<item: null>
                child 0, item: null
              slug: string
              to
              {'_id': {'$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': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$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

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Dataset Card for CANOE Multimodal (MCAP)

A FiftyOne build of CANOE (Canadian Aquatic Navigation for Observation of the Environment), a multi-sensor marine navigation dataset collected by ASRL (UTIAS) on an uncrewed surface vessel (USV). This build repackages 4 of CANOE's 8 public sequences as time-synchronized MCAP recordings for FiftyOne's native multimodal dataset support (FiftyOne 1.19+). Each sample is one episode, viewable in FiftyOne's tiled multimodal viewer with synchronized stereo camera, radar, sonar, lidar point cloud, GPS/INS track, and IMU/motor/velocity telemetry.

CANOE itself is a raw multi-sensor dataset for benchmarking odometry/localization algorithms in aquatic environments — it ships no object/semantic annotations, only dense ground-truth 6-DoF pose from post-processed GNSS/INS. This repackaging does not add or alter any ground truth; see Dataset Creation for exactly what was kept, converted, and left out.

This is a FiftyOne dataset with 4 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("harpreetsahota/canoe-multimodal")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

CANOE is a multi-sensor marine navigation dataset collected on an uncrewed surface vessel (USV) traversing a lake and a reservoir in Ontario, Canada, over three days (8 public sequences, 14+ hours, 50 km). Sensor suite: a 360° Navtech RAS6 radar (dual range modes), a 128-beam Ouster OS1 lidar with integrated IMU, a Teledyne Bumblebee stereo camera, an Oculus M3000d imaging sonar, dual Starfish 990F sidescan sonar, motor power sensors, and a NovAtel OEM7 GNSS/INS post-processed to ~2-4 cm RMS position accuracy. The intended purpose is benchmarking odometry and localization algorithms for aquatic navigation; there are no object/semantic annotations, only raw sensor streams plus dense ground-truth pose. This FiftyOne build covers 4 of the 8 public sequences, chosen for sensor/environment diversity (see Curation Rationale).

Curated by: Autonomous Space Robotics Laboratory (ASRL), University of Toronto Institute for Aerospace Studies (UTIAS) — original CANOE data collection, sensor calibration, and ground-truth pose post-processing. This MCAP/FiftyOne multimodal repackaging (episode authoring, dataset card) was prepared independently by Harpreet Sahota.
Funded by: [More Information Needed]
Shared by: Harpreet Sahota (this repackaging); the original CANOE dataset is shared by ASRL via the AWS Open Data Registry (s3://canoe-data) and https://www.canoedataset.com.
Language(s): N/A (sensor data — radar, lidar, imagery, sonar, GPS, IMU; no text).
License: CC BY 4.0, inherited from the source CANOE release.

Dataset Sources

Repository (devkit): https://github.com/utiasASRL/pycanoe
Paper: [More Information Needed] — no published paper is referenced by the AWS Open Data Registry listing, the devkit, or https://www.canoedataset.com at the time of this card; see Citation for the registry's suggested access citation.
Demo: https://www.canoedataset.com (official docs/download/leaderboard site) and https://registry.opendata.aws/canoe/ (AWS Open Data listing)

Uses

Direct Use

Exercising and demoing FiftyOne's multimodal MCAP support: synchronized playback of stereo camera, decoded radar/sonar pseudo-imagery, 3D lidar point cloud, GPS/map track, and IMU/motor/velocity telemetry across real aquatic robotics recordings.
Browsing/inspecting USV traversals of a lake (short and long routes) and a reservoir, including both radar operating modes (1000 m long-range and 150 m Doppler).
Prototyping odometry/localization analyses against the dense ground-truth pose (/tf, /odom, /gps, /velocity) provided at each sensor's native timestamp.

Out-of-Scope Use

Reproducing CANOE's official odometry/localization benchmark (the SE(2)/SE(3) Lat/Long/Yaw RMSE leaderboard on canoedataset.com) — that protocol operates over full, un-windowed sequences; this build covers only 4 of the 8 public sequences, 3 of which are short fixed-duration windows (30-60s), not full traversals.
Any task requiring object/semantic annotations — CANOE ships none (purely raw sensor streams plus ground-truth pose).
Commercial or non-commercial use inconsistent with the CC BY 4.0 attribution requirement.

Dataset Structure

This is a flat (ungrouped) FiftyOne dataset with media_type: "multimodal" and 4 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, radar, sonar, lidar, GPS, IMU, motor) 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`	`route`	Coverage	`radar_max_range_m`
`canoe-2025-08-21-19-16`	Sample	Full sequence (~72s active sensors; GPS/INS logs continuously for ~581s)	1000 (long-range)
`canoe-2025-08-20-16-07`	Day 1 – Lake Short	30s window	1000 (long-range)
`canoe-2025-08-20-18-07`	Day 1 – Lake Long	60s window	1000 (long-range)
`canoe-2025-08-28-14-13`	Reservoir Doppler 1	60s window	150 (Doppler)

route gives the human-readable route name from canoedataset.com's Download tab. The Sample sequence is the only one downloaded and authored in full — at 8.9 GB it is small enough to skip windowing; it is also the only episode where the GNSS/INS trace outlasts the active camera/lidar/radar/sonar capture window (continuous INS logging vs. a shorter active-sensor window), which is expected, not a data error. The other 3 episodes are fixed-duration windows starting at the earliest sensor timestamp for that sequence. 4 of the 8 public CANOE sequences are not (yet) included in this build: Day 2 – Lake Short, Day 2 – Lake Long, Reservoir Doppler 2, and Reservoir 3.

Fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to the episode's merged `.mcap` file — the sample's multimodal media
`tags`	`ListField(StringField)`	Empty for every sample in this build
`sequence_id`	`StringField`	Raw CANOE sequence ID (`canoe-YYYY-MM-DD-HH-MM`), verbatim from the S3 folder name
`route`	`StringField`	Human-readable route label, from the Download tab at https://www.canoedataset.com/#/download
`radar_max_range_m`	`IntField`	Radar operating mode for this sequence's data: `1000` (long-range) or `150` (Doppler), from `calib/radar_config.yaml`
`duration_s`	`FloatField`	Episode duration in seconds, computed from the merged 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 (13 for every episode in this build)
`topics`	`ListField(StringField)`	Every MCAP topic present: `/cam_left`, `/cam_left/calibration`, `/cam_left_lidar_overlay`, `/cam_right`, `/gps`, `/imu`, `/lidar`, `/motor`, `/odom`, `/radar`, `/sonar`, `/tf`, `/velocity`
`schemas`	`ListField(StringField)`	Every distinct Foxglove schema present: `foxglove.CameraCalibration`, `foxglove.CompressedImage`, `foxglove.FrameTransform`, `foxglove.LocationFix`, `foxglove.Odometry`, `foxglove.PointCloud`, `foxglove.RawImage`, plus one generic JSON schema shared by `/imu`, `/motor`, `/velocity`
`has_image`	`BooleanField`	Whether the episode has an Image-tile-decodable stream (`foxglove.CompressedImage` or `foxglove.RawImage`) — `True` for every episode (cameras, and the decoded radar/sonar pseudo-images)
`has_pointcloud`	`BooleanField`	Whether it has a decodable point-cloud stream for the 3D tile (`foxglove.PointCloud`, i.e. lidar) — `True` for every episode
`has_gps`	`BooleanField`	Whether it has a decodable GPS fix stream for the Map tile (`foxglove.LocationFix`) — `True` for every episode
`has_imu`	`BooleanField`	Whether it has a schema-decoded IMU stream for a dedicated Plot tile — `False` for every episode in this build, even though a `/imu` topic exists (see Parsing decisions)
`has_logs`	`BooleanField`	Whether it has a decodable log stream for the Logs tile — `False` for every episode (CANOE has no log messages)
`source`	`StringField`	Constant description string: `"CANOE (utiasASRL) - lake/reservoir USV traversal"`

Standard FiftyOne bookkeeping fields (id, metadata, created_at, last_modified_at) are also present but not source-specific.

Label types and why

No FiftyOne label fields are attached to this dataset. CANOE ships no object/semantic/classification annotations — the actual multimodal content (camera images, decoded radar/sonar imagery, lidar point cloud, GPS track, and telemetry) lives entirely inside each sample's .mcap file and is decoded live by FiftyOne's multimodal viewer (Image, 3D, Map, and Plot/Message tiles), not represented as separate Detection/Segmentation/etc. label objects. The has_* boolean fields above exist purely so users can filter or query episodes by which tiles will populate with real data, without opening every MCAP file first — e.g. dataset.match(F("has_image") & F("has_gps")).

Parsing decisions

One sample = one episode. Each sample is a single, continuous window of sensor data, never split into per-frame samples — FiftyOne's multimodal viewer handles playback within an episode.
Full sequences are far too large for one MCAP episode (from ~714 GB / ~1.7 hours for the shortest example sequence, to considerably more for the longer-route sequences), so 3 of the 4 episodes are fixed-duration (30-60s) windows starting at the earliest timestamp across all sensor folders for that sequence, downloaded via targeted S3 requests rather than a full sequence sync. The Sample sequence (8.9 GB total) was downloaded and authored in full instead, since it is small enough to skip windowing entirely.
Lidar's fixed-size preallocated array is filtered. Each .bin file preallocates a 131,072-point array per frame; most entries are (0,0,0) "no return" placeholders. Points are filtered to range > 0.5 m before being packed into the foxglove.PointCloud message (roughly 70-93% of raw points are dropped per frame across these episodes), otherwise the 3D tile shows a dense blob at the origin.
Radar and sonar have no native MCAP polar-sensor schema. Both are decoded from their raw Oxford-style/encoder-embedded PNG encoding via the pycanoe devkit's polar_to_cart(), clipped and normalized to [0, 255] uint8, and logged as foxglove.RawImage (mono8) pseudo-images — not real camera views, but the closest available Image-tile representation of a polar scan.
GPS/lat-lon lives only in a giant whole-sequence file. novatel/novatel_original.csv is interpolated to 1000 Hz and can be up to ~3 GB per sequence. For the 3 windowed episodes, only the matching time window was extracted via a targeted HTTP Range GET (estimating the byte offset from the observed row rate) rather than downloading the full file. For the Sample episode (downloaded in full), the same raw file is parsed directly, skipping its free-text metadata header block that precedes the actual CSV header row.
Ground-truth pose and velocity (/tf, /odom, /velocity) use the lidar frame's interpolated pose as the representative boat state, logged at every lidar frame's timestamp from novatel/lidar_poses.csv. /odom carries the full foxglove.Odometry message (pose + ENU/body linear and angular velocity); /velocity duplicates the same velocity/body-rate values as a flat JSON dict for convenient Plot-tile inspection without decoding Odometry.
/imu, /motor, and /velocity are logged as generic JSON messages, not foxglove.Imu or another schema-decoded type — this is why has_imu is False for every episode despite an /imu topic being present. They still populate FiftyOne's Message/Plot tile via raw JSON, just without a dedicated schema-typed decoder.
Sensors are not hardware-synchronized. Two separate onboard computers drive different sensor groups (radar/lidar/camera vs. sonar/sidescan/motor/GPS); only GPS, radar, and lidar are tightly PTP-synced. Real per-sensor timestamps are used as-is, which is correct/expected behavior rather than an alignment bug.
Lidar is also projected onto the left camera image, matching the pycanoe devkit's own tutorials/view_calib.ipynb "Lidar-(Left) Cam Overlay" cell (transform lidar points into the cam_left frame via calib/T_cam_left_lidar.txt, keep forward-facing points within 50 m, project with calib/P_cam.txt, color by depth). Two ways to view this are provided: a pre-baked /cam_left_lidar_overlay JPEG stream (always visible, no configuration needed, depth-colored jet colormap like the devkit's static plots) and a /cam_left/calibration (foxglove.CameraCalibration) message that lets FiftyOne's multimodal viewer project the live /lidar point cloud onto the plain /cam_left tile via its built-in "Pointcloud projections" toggle, and draw cam_left's frustum in the 3D tile. Both read the same calibration and agree; only the rendering path (pre-baked image vs. live GPU projection) differs. No calibration message is published for /cam_right, since P_cam.txt/T_cam_left_lidar.txt are only valid for the left camera.
Sidescan sonar is out of scope for this build. sidescan.xtf/sidescan.csv aggregate all pings for a whole sequence into single files with no per-frame breakdown; they are not decoded into any MCAP channel here (only the small waterfall-preview.png was kept as a representative preview asset during recon, and is not part of the MCAP itself).

Dataset Creation

Curation Rationale

Full CANOE sequences are enormous — one example sequence (Day 1 – Lake Short) alone is 297,684 objects / ~714 GB spanning ~1.7 hours, and the longer-route sequences (e.g. Day 1 – Lake Long) are larger still — and the public --no-sign-request S3 bucket allows arbitrary slicing, so downloading all 8 sequences in full was not practical for a lightweight FiftyOne showcase. 4 episodes were selected for sensor/environment diversity instead of exhaustive coverage: the officially curated, all-sensor "Sample" sequence in full (small enough at 8.9 GB to skip windowing), plus fixed-duration windows of two lake routes and one reservoir route — the reservoir episode was specifically chosen to also exercise the radar's 150 m Doppler mode, distinct from the 1000 m long-range mode used everywhere else.

Source Data

Data Collection and Processing

Per ASRL's dataset documentation (DATA_REFERENCE.md in the pycanoe devkit, mirrored on canoedataset.com): data was collected on a lake and reservoir in Ontario, Canada, over three days, traversing a "short" and "long" lake route (each twice) and a reservoir route (three times) — over 14 hours and 50 km total across all 8 public sequences. Sensor suite: Navtech RAS6 360° radar (4 Hz, 400 azimuths/rotation, dual range modes: 1000 m long-range at 0.292 m/bin, or 150 m Doppler at 0.0438 m/bin), 128-beam Ouster OS1 lidar with integrated IMU (10 Hz rotation, >1M points/sec, strongest-return only), Teledyne Bumblebee rectified stereo camera (10 Hz, 1152×2048), Oculus M3000d imaging sonar (~14 Hz, 512 beams × 378 range bins, 30 m max range), dual Starfish 990F sidescan sonar (not included in this build), motor power sensors (2.5 Hz), and a NovAtel OEM7 GNSS/INS post-processed with Inertial Explorer (using base-station corrections) to ~2-4 cm RMS position accuracy, interpolated to 1000 Hz.

For this repackaging: raw per-frame sensor files were downloaded from the public s3://canoe-data bucket (targeted time-windowed slices for 3 of the 4 episodes; a full sync for the Sample sequence), decoded with the official pycanoe devkit (Oxford-style polar decoding for radar/sonar, custom binary parsing for lidar, ENU-frame pose/velocity interpolation from the NovAtel pose files), and packed into one merged .mcap file per episode using foxglove-sdk. No sensor data was synthesized, relabeled, or altered beyond the conversions and filtering documented in Parsing decisions.

Who are the source data producers?

Autonomous Space Robotics Laboratory (ASRL), University of Toronto Institute for Aerospace Studies (UTIAS) — original CANOE data collection, sensor calibration, and ground-truth pose post-processing.

Annotations

Annotation process

None. CANOE ships no object/semantic/classification labels — it is a raw-sensor-plus-ground-truth-pose dataset intended for SLAM/odometry benchmarking. This is corroborated by canoedataset.com's Leaderboard tab (currently empty, "Check back soon!"), whose column schema — Method, Ref Sensor, Test Sensor, Lat RMSE, Long RMSE, Yaw RMSE, Consistency, Runtime, Environment, split into SE(2)/SE(3) Odometry tabs — is purely pose-error metrics, not annotation-based.

Who are the annotators?

N/A — no annotations exist. The closest artifact to a label is the post-processed NovAtel GNSS/INS ground-truth pose, produced by ASRL using NovAtel's Inertial Explorer software suite (see Data Collection and Processing).

Personal and Sensitive Information

None identified. All sensor streams (radar, lidar, camera, sonar, GPS, IMU, motor) record an uncrewed surface vessel's traversal of a lake and reservoir. Camera imagery may incidentally capture other lake/reservoir users, boats, or shoreline structures visible from the water, but this repackaging performs no additional processing, re-identification, or redaction beyond what ASRL already released publicly. ASRL's own DATA_LICENSE.md notes that privacy concerns, including requests to remove personally identifiable information, can be sent to canoe@robotics.utias.utoronto.ca.

Citation

No published paper or BibTeX entry exists for CANOE at the time of this card. The AWS Open Data Registry listing gives the following suggested access citation:

BibTeX:

[More Information Needed]

APA:

Autonomous Space Robotics Laboratory (ASRL), University of Toronto Institute for Aerospace Studies. (n.d.). CANOE (Canadian Aquatic Navigation for Observation of the Environment) Dataset. Registry of Open Data on AWS. Retrieved from https://registry.opendata.aws/canoe/

"CANOE (Canadian Aquatic Navigation for Observation of the Environment) Dataset was accessed on DATE from https://registry.opendata.aws/canoe." — suggested citation text per the AWS Open Data Registry listing.

More Information

This repository is an independently-curated, derived subset of the official CANOE dataset, repackaged as MCAP for FiftyOne's multimodal support. It is not an official CANOE/ASRL artifact. For the full dataset (all 8 public sequences, full un-windowed traversals, the devkit, and the odometry/localization leaderboard), see https://www.canoedataset.com, https://registry.opendata.aws/canoe/, and https://github.com/utiasASRL/pycanoe.

Dataset Card Authors

Harpreet Sahota (@harpreetsahota) — MCAP repackaging and this card. Original dataset producers are listed under Dataset Description.

Dataset Card Contact

Harpreet Sahota — https://huggingface.co/harpreetsahota

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