Datasets:

Voxel51
/

yuto-mms-multimodal

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 436 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 424 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
      child 2, tags: list<item: null>
          child 0, item: null
      child 3, _media_type: string
      child 4, _rand: double
      child 5, episode_tag: string
      child 6, duration_s: double
      child 7, message_count: int64
      child 8, channel_count: int64
      child 9, topics: list<item: string>
          child 0, item: string
      child 10, schemas: list<item: string>
          child 0, item: string
      child 11, has_image: bool
      child 12, has_pointcloud: bool
      child 13, has_gps: bool
      child 14, has_imu: bool
      child 15, sequence_id: string
      child 16, route_length_m: int64
      child 17, collection_site: string
      child 18, license_note: string
      child 19, _dataset_id: struct<$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, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targets: null
skeletons: null
camera_intrinsics: null
static_tra
...
string, fields:  (... 318 chars omitted)
  child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
      child 0, name: string
      child 1, ftype: string
      child 2, embedded_doc_type: string
      child 3, subfield: string
      child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
              child 0, name: string
              child 1, ftype: string
              child 2, embedded_doc_type: null
              child 3, subfield: null
              child 4, fields: list<item: null>
                  child 0, item: null
              child 5, db_field: string
              child 6, description: null
              child 7, info: null
              child 8, read_only: bool
              child 9, created_at: struct<$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, $date: string
sample_collection_name: string
default_classes: list<item: null>
  child 0, item: null
_id: struct<$oid: string>
  child 0, $oid: string
frame_fields: list<item: null>
  child 0, item: null
to
{'_id': {'$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:  (... 436 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 424 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, episode_tag: string
                    child 6, duration_s: double
                    child 7, message_count: int64
                    child 8, channel_count: int64
                    child 9, topics: list<item: string>
                        child 0, item: string
                    child 10, schemas: list<item: string>
                        child 0, item: string
                    child 11, has_image: bool
                    child 12, has_pointcloud: bool
                    child 13, has_gps: bool
                    child 14, has_imu: bool
                    child 15, sequence_id: string
                    child 16, route_length_m: int64
                    child 17, collection_site: string
                    child 18, license_note: string
                    child 19, _dataset_id: struct<$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, $date: string
              group_media_types: null
              info: null
              app_config: null
              classes: null
              mask_targets: null
              default_mask_targets: null
              skeletons: null
              camera_intrinsics: null
              static_tra
              ...
              string, fields:  (... 318 chars omitted)
                child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
                    child 0, name: string
                    child 1, ftype: string
                    child 2, embedded_doc_type: string
                    child 3, subfield: string
                    child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
                            child 0, name: string
                            child 1, ftype: string
                            child 2, embedded_doc_type: null
                            child 3, subfield: null
                            child 4, fields: list<item: null>
                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$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, $date: string
              sample_collection_name: string
              default_classes: list<item: null>
                child 0, item: null
              _id: struct<$oid: string>
                child 0, $oid: string
              frame_fields: list<item: null>
                child 0, item: null
              to
              {'_id': {'$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 YUTO MMS Multimodal (MCAP)

A FiftyOne build of YUTO MMS (York University Teledyne Optech Mobile Mapping System Dataset), a SLAM benchmark from the AUSM Lab at York University. This build repackages the source dataset's per-sequence raw sensor folders as time-synchronized MCAP recordings for FiftyOne's native multimodal dataset support (FiftyOne 1.19+). Each sample is one episode (one continuous drive), viewable in FiftyOne's tiled multimodal viewer with a synchronized panoramic-camera image, a real-RGB-colorized LiDAR point cloud, a progressively-accumulated world-frame map point cloud, GPS/INS fix, and IMU telemetry.

YUTO MMS ships 4 sequences (A/B/C/D, ~134 GB total on Zenodo); this build currently includes 2 episodes (Sequences A and D), with B and C in progress (see Curation Rationale). The source dataset carries no object/semantic/segmentation labels of any kind — it is raw sensor data plus a photogrammetric 6-DOF ground-truth camera trajectory, intended for SLAM/odometry benchmarking, not perception tasks. This repackaging does not add, remove, or alter any ground truth; every coordinate transform applied (LiDAR→IMU, world→IMU) is derived from the source dataset's own real calibration files, never fabricated — see Dataset Creation for exactly what was computed and how it was verified.

This is a FiftyOne dataset with 2 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/yuto-mms-multimodal")

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

Dataset Details

Dataset Description

YUTO MMS was collected with Teledyne Optech's Maverick mobile mapping system (<9 kg, roof-rack mountable) across two collection days: Sequence A at the Teledyne Optech headquarters parking lot in Vaughan, Ontario (June 21, 2019), and Sequences B, C, D around York University's Keele Campus in Toronto (August 12, 2020) — a main-campus loop (B), the adjacent York Village residential neighborhood (C), and a residential-area loop (D). All four were recorded under sunny weather. The Maverick rig combines a Velodyne HDL-32E LiDAR (32-beam, 360° horizontal FOV, tilted 45° from the camera's optical axis, ~15.26 revolutions/s, ±3 cm absolute accuracy) with a Ladybug 5 panoramic camera (six Sony ICX655 CCD sensors stitched into one 8000×4000 equirectangular image, ~7.5 FPS) and a NovAtel SPAN-IGM-S1 GNSS/IMU (STIM300 IMU + NovAtel OEM615 receiver, GPS/IMU at 125 Hz). Ground-truth camera trajectories were produced offline via Teledyne Optech's LMS Pro software (RTK GPS/IMU + photogrammetric bundle adjustment), reporting cm-level accuracy (planar-fit RMS 0.010 m over ~7.9M calibration points, per the source paper's Table 5).

Curated by: AUSM Lab, Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University — Yujia Zhang, SeyedMostafa Ahmadi, Jungwon Kang, Zahra Arjmandi, Gunho Sohn (corresponding author). 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), grant CRDPJ 537080-18.
Shared by: Harpreet Sahota (this repackaging); the original YUTO MMS dataset is shared by the AUSM Lab via ausmlab.github.io/yutomms and Zenodo.
Language(s): N/A (sensor data — panoramic camera, LiDAR, GPS/INS, IMU; no text).
License: Conflicting. The website/README/tool page all state CC BY-NC-SA 4.0. The live Zenodo record for every sequence checked directly (A: 13203376; B: 13235145, 10576909, 10576911, 10577975; C: 10556128, 10556151, 10559929, 10574428, 10575667, 10575673, 10575677; D: 10560181, 10570781, 10572097 — via each record's own rel="license" HTTP header) instead serves CC BY 4.0. These have materially different terms (NC/SA vs. none) — verify current status before redistributing.

Dataset Sources

Repository: ausmlab/yutomms (site source); devkits: MaverickProjectLidar2Image (LiDAR→panorama projection, Matlab); data_player is linked from the site as "code for generating rosbag" but is a generic MulRan-dataset ROS player whose on-disk layout does not match YUTO MMS's real files — not YUTO-specific despite the link.
Paper: Zhang, Y., Ahmadi, S., Kang, J., Arjmandi, Z., & Sohn, G. (2024). YUTO MMS: A comprehensive SLAM dataset for urban mobile mapping with tilted LiDAR and panoramic camera integration. The International Journal of Robotics Research, 44(1), 3–21. doi:10.1177/02783649241261079 (paywalled on SAGE; free full text at PMC11685038).
Demo: ausmlab.github.io/yutomms (official site, download page, and devkit links).

Uses

Direct Use

Exercising/demoing FiftyOne's multimodal MCAP support: synchronized playback of a panoramic-camera image, a colorized 3D LiDAR point cloud, a growing accumulated map, a GPS/INS map track, and IMU telemetry from a real mobile-mapping-system recording.
SLAM/odometry algorithm prototyping and evaluation against a real, photogrammetrically-derived 6-DOF ground-truth camera trajectory (/tf's world→camera transform).
Studying tilted-LiDAR-specific SLAM challenges — restricted/non- rectangular camera-LiDAR overlap and sparse upper-beam coverage — which is the entire motivation for the source dataset's existence (per the source paper's Introduction).
Inspecting real-RGB-colorized LiDAR points (each point sampled directly from its nearest-in-time panorama via the dataset's own LiDAR↔camera boresight, not a synthetic colormap) and the /lidar_map topic's progressively-accumulated world-frame map, useful for visually verifying a calibration chain end-to-end.

Out-of-Scope Use

Object detection, segmentation, classification, or any perception task — no such labels exist anywhere in the source dataset. The source paper's entire "Dataset" section (images, LiDAR, GPS/IMU, calibration, ground-truth trajectory) contains no annotation pipeline of any kind.
Reproducing the source paper's SLAM benchmark (Tables 7–8: ORB-SLAM2, VINS-Mono, RPV-SLAM, HDPV-SLAM, LOAM, Google Cartographer, PVL-Cartographer ATE/RTE/RRE) — this build repackages raw sensor streams into MCAP episodes; it does not run or reproduce any SLAM system.
Assuming a shipped per-image depth map exists. The source paper (Section 5.3.4) states one JPG depth map ships per panoramic image, but no such files exist in the real downloaded/listed contents of any of the 4 sequences (verified directly — only PanoramicImages/*.jpg is present; see Data Collection and Processing).
Assuming a shipped camera↔LiDAR synchronization file exists. The source paper (Section 5.3.2) describes a dedicated sync .txt, but no such file is present in any real downloaded sequence — GroundTruth_*.txt's Filename column is the only real join key between modalities.
Sub-episode or per-frame sampling as independent, i.i.d. samples — each sample is one continuous drive; camera, LiDAR, IMU, and GPS/INS are asynchronous multi-rate streams of the same trajectory, not synchronized single-instant frames.

Dataset Structure

This is a flat (ungrouped) FiftyOne dataset with media_type: "multimodal" and 2 samples. Each sample is one episode, stored as one .mcap file covering an entire sequence's continuous drive; FiftyOne infers the multimodal media type automatically from the .mcap extension. There is no per-frame image or point-cloud sample — the episode is the sample unit, and every stream inside it 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`	`collection_site`	`route_length_m`	Duration	`message_count`	`channel_count`
A	ep000	Teledyne Optech headquarters, Vaughan, Ontario, Canada	324	94.6s	15,942	6
D	ep000	York University Keele Campus, Toronto	3634	1296.0s	348,726	6

Sequence A's episode spans its full recording uniformly (all topics cover the same ~94.6s). Sequence D's episode is not uniform: /camera, /lidar, and /lidar_map only span the first ~630s (10.5 min — the Ladybug camera and LiDAR genuinely stopped recording partway through, verified against the raw ground-truth and LiDAR-timestamp files), while /imu and /gps continue for the full 1296s (21.6 min, verified against the raw .bag_imu.csv/.bag_ins.csv files directly) — this is real logged data, not a bug in this repackaging.

Fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to the episode's `.mcap` file — the sample's multimodal media
`sequence_id`	`StringField`	Source sequence letter (`A`, `B`, `C`, or `D`), verbatim from the source dataset's naming
`episode_tag`	`StringField`	Local identifier for the episode within a sequence (`ep000` — one episode per sequence, no splitting)
`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 (6 for every episode)
`topics`	`ListField(StringField)`	Every MCAP topic present (see MCAP topics 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`) — `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` for every episode
`has_imu`	`BooleanField`	Whether it has an `/imu` topic (raw JSON, Plot-tile only) — `True` for every episode
`route_length_m`	`IntField`	Real driven route length in meters, from the source paper's Table 3
`collection_site`	`StringField`	Real-world collection site, verified per-sequence from the shipped ground-truth filename (`_HQ_` vs `_YU_campus_`) and the source paper — not assumed uniform across sequences (Sequence A differs from B/C/D)
`license_note`	`StringField`	Per-sequence license-conflict note (see License)

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

MCAP topics (inside each episode)

Topic	Schema	Tile	Notes
`/camera`	`foxglove.CompressedImage` (jpeg)	Image	Panoramic image, downsampled 0.5× + re-encoded at JPEG quality 85 for embed size only — the colorization below always samples from the full-resolution source file, so this doesn't touch point-cloud fidelity
`/lidar`	`foxglove.PointCloud`	3D	Per-scan point cloud in the IMU frame: `x,y,z,intensity` (float32) + real `red,green,blue` (uint8) sampled from the nearest-in-time panorama via the LiDAR↔camera boresight — not a synthetic colormap. Points with no valid panorama projection fall back to greyscale-from-intensity (never triggered on Sequences A or D: 100% of points projected in-bounds on both)
`/lidar_map`	`foxglove.PointCloud`	3D	Growing world-frame accumulation of `/lidar`, voxel-deduplicated (0.2 m for A, 0.75 m for D — tuned per-sequence, not shared) and logged at a fixed rate (2 Hz for A, 0.5 Hz for D) so scrubbing forward shows the map built up "as of" that time, with no fabricated points
`/imu`	generic JSON	Plot	Raw accelerometer (`ax,ay,az`, m/s²) + gyroscope (`gx,gy,gz`, rad/s)
`/gps`	`foxglove.LocationFix`	Map	INS lat/lon/altitude, already WGS-84 (no projection needed), plus `status`/`service`/`position_cov_type` as metadata key-value pairs
`/tf`	`foxglove.FrameTransform`	(enables 3D)	`world`→`camera` (real photogrammetric pose, from the ground-truth `.txt`) and `world`→`imu` (derived: the real camera pose composed with the real static camera↔IMU boresight from the sequence's own calibration file — not independently measured, not fabricated)

Label types and why

No FiftyOne sample-level label fields (Detections, Classifications, etc.) are attached, and none should be — the source dataset has no object/semantic/segmentation annotations of any kind (verified against the source paper, whose entire "Dataset" section covers only raw sensor streams and the ground-truth trajectory). The closest thing to a "label" is the per-image 6-DOF camera pose, which is carried as the /tf topic's world→camera transform (a continuous-time stream, not a fixed-length list a sample-level field could hold) rather than as a sample field.

Parsing decisions

One sample = one episode, one .mcap per sequence's entire continuous drive — no per-frame or per-message splitting.
Calibration is parsed fresh from each sequence's own shipped *_LCP.LCP/*_CCP.ccp files, never hardcoded from another sequence. This was necessary, not theoretical: Sequence A's calibration unit ID is 6100012; Sequences B/C/D's is 6100018, with boresight angles differing by several degrees (different collection day/rig calibration). Independently cross-checked: the source paper's own Table 4 lists the B/C/D LiDAR boresight rotation as [179.579, -44.646, 0.601] degrees — matching this repackaging's parsed value from Sequence D's real .LCP file to 3 decimal places.
world→imu is derived, not independently measured. Only the camera has a directly-measured absolute pose (from the ground-truth .txt). world→imu is computed by composing that real camera pose with the real static camera↔IMU boresight from the same calibration file — verified by checking the derived IMU position stays a constant lever-arm offset from the camera position across the whole sequence (a rigid rig requires exactly that).
LiDAR filename-to-timestamp convention differs by sequence. Sequence A's .bin filenames are the GPS-time-of-week in nanoseconds directly; Sequence D's are a sequential capture index, with real timestamps in a sibling LidarScanTimestamp.txt (one line per scan). The convention is auto-detected per sequence (checks for that file first), not assumed.
Depth maps and a camera↔LiDAR sync file, both described in the source paper, are not included — neither exists in any real downloaded sequence (see Out-of-Scope Use); nothing was fabricated to fill that gap.

Dataset Creation

Curation Rationale

The full YUTO MMS dataset is 4 sequences across 15 separate Zenodo records, ~134 GB total (A: 3.8 GB; B: 45.6 GB, 4 parts; C: 59.2 GB, 7 parts; D: 25.2 GB, 3 parts). Sequence A was authored first because, at under 4 GB, it contains every modality and file type the other sequences do (per the source devkit's own directory diagram), making it sufficient to fully characterize the format before committing to the larger sequences. Sequence D was authored second. Sequences B and C are in progress — larger sequences require re-deriving the /lidar_map voxel-size/update-rate tuning per sequence (output size scales with route length/duration, not just point count — reusing Sequence A's tuning on Sequence D during authoring produced an oversized intermediate file before this was caught and fixed) rather than reusing a previous sequence's settings.

Source Data

Data Collection and Processing

Per the source paper: Sequence A was collected at the Teledyne Optech headquarters parking lot in Vaughan, Ontario on June 21, 2019 (sunny, ~3.4 m/s average driving speed, no dynamic objects — a controlled lot, one small 324 m loop). Sequences B, C, and D were collected around York University's Keele Campus in Toronto on August 12, 2020 (sunny, ~6.1 m/s average driving speed, dynamic objects present — cars, pedestrians, cyclists on public roads): B is a 7035 m main-campus loop plus several smaller loops, C is a 9137 m route through the adjacent York Village residential neighborhood with many medium loops, and D is a 3634 m route through open/residential areas with a large loop plus smaller loops. Ground-truth camera trajectories were produced offline by Teledyne Optech's LMS Pro software: RTK-corrected GPS/IMU plus a photogrammetric bundle block adjustment (automated tie-point measurement, boresight/interior-orientation corrections), reported at cm-level precision (planar-fit RMS 0.010 m, std 0.014 m over ~7.9M points, per the source paper's Table 5).

For this repackaging: raw per-sequence files (panoramic JPGs, raw .bin LiDAR point dumps, GPS/INS and IMU CSVs, calibration .LCP/.ccp XML, and the ground-truth .txt) were downloaded directly from Zenodo, parsed with a from-scratch Python script (not the source devkit's Matlab code, though its projection/boresight math was replicated and cross-checked against it), and packed into one .mcap file per episode using the foxglove-sdk. LiDAR points were additionally colorized with real RGB (sampled from the nearest-in-time panorama via the same LiDAR↔camera boresight the source devkit's own projection script uses) and accumulated into a world-frame map topic — see MCAP topics. No sensor data or ground truth was synthesized, relabeled, or altered beyond the coordinate-frame compositions documented in Parsing decisions.

Who are the source data producers?

AUSM Lab, Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University — original data collection, sensor calibration, and ground-truth trajectory post-processing (via Teledyne Optech's LMS Pro software).

Annotations

Annotation process

None. No manual or automated object/semantic annotation exists anywhere in this dataset. The only "ground truth" is the photogrammetrically post-processed 6-DOF camera trajectory described above, which is a sensor-fusion/bundle-adjustment output, not a manual annotation.

Who are the annotators?

N/A — no annotation process exists (see above).

Personal and Sensitive Information

The source dataset's panoramic images were captured while driving through public roads and a university campus during regular daytime activity (dynamic objects present on B/C/D: cars, pedestrians, cyclists). No statement about face/license-plate anonymization is made in the source paper or website, unlike some comparable driving datasets — this repackaging performs no additional processing, anonymization, or redaction beyond what the AUSM Lab already released publicly.

Citation

BibTeX:

@article{zhang2024yutomms,
  author  = {Yujia Zhang and SeyedMostafa Ahmadi and Jungwon Kang and
             Zahra Arjmandi and Gunho Sohn},
  title   = {YUTO MMS: A comprehensive SLAM dataset for urban mobile
             mapping with tilted LiDAR and panoramic camera integration},
  journal = {The International Journal of Robotics Research},
  volume  = {44},
  number  = {1},
  pages   = {3--21},
  year    = {2024},
  doi     = {10.1177/02783649241261079}
}

APA:

Zhang, Y., Ahmadi, S., Kang, J., Arjmandi, Z., & Sohn, G. (2024). YUTO MMS: A comprehensive SLAM dataset for urban mobile mapping with tilted LiDAR and panoramic camera integration. The International Journal of Robotics Research, 44(1), 3–21. https://doi.org/10.1177/02783649241261079

More Information

This repository is an independently-curated MCAP/FiftyOne repackaging of the official YUTO MMS dataset. It is not an official AUSM Lab/York University artifact, and currently covers 2 of the source dataset's 4 sequences (see Curation Rationale). For the full dataset (all 4 sequences, the Matlab devkit, and the original directory layout), see ausmlab.github.io/yutomms and github.com/ausmlab/yutomms.

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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