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metadata 
language:
  - en
license: mit
size_categories:
  - 1<n<10
pretty_name: OctoSense (FiftyOne Multimodal)
tags:
  - fiftyone
  - multimodal
  - mcap
  - robotics
dataset_summary: |
  OctoSense is a time-synchronized, calibrated multi-sensor robot perception
  dataset captured by an open-source eight-sensor rig (stereo RGB + event
  cameras, thermal, Ouster LiDAR, IMU, RTK GPS, CAN bus/joint state) on three
  platforms: a car (371 sequences, 59 h, 2 474 km), a boat (9 sequences), and
  a Unitree Go2-W quadruped (2 sequences). This FiftyOne version packages eight
  representative episodes as MCAP files — one sample per episode — with all
  sensor streams time-aligned on a shared playback clock. Episodes cover all
  three platforms, every optional modality, and both train/test splits.

  ## Installation

  ```bash
  pip install -U fiftyone
  ```

  ## Usage

  ```python
  import fiftyone as fo
  import fiftyone.utils.huggingface as fouh

  dataset = fouh.load_from_hub("Voxel51/OctoSense")
  session = fo.launch_app(dataset)
  ```

Dataset Card for OctoSense (FiftyOne Multimodal)

image/png

OctoSense is a time-synchronized, calibrated multi-sensor robot perception dataset captured by an open-source eight-sensor rig on three platforms: a car, a boat, and a Unitree Go2-W quadruped. This FiftyOne version packages eight representative episodes as MCAP files — one sample per episode — loadable directly in the FiftyOne App's multimodal viewer with synchronized playback across all sensor streams.

Installation 

pip install -U fiftyone

Usage 

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("Voxel51/OctoSense")
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

OctoSense provides 59 hours of time-synchronized driving data from eight sensors: stereo RGB and event cameras, a thermal camera, an Ouster OS1-64 LiDAR, an IMU, RTK-corrected GPS, and vehicle CAN bus (car) or joint-angle proprioception (quadruped). Sensors are hardware PPS-locked to a single clock and post-processed with Kalman-smoothed affine synchronization. All sensors record at native rates without dropped data: the RGB pair at 100 Hz, event cameras at up to ~7 MEv/s, LiDAR at 10 Hz, IMU at 400 Hz (VectorNav) and 100 Hz (in-LiDAR), and GPS at 5 Hz. Car sequences additionally include ego-motion optical flow, dense LiDAR-projected depth, pseudo-label semantic segmentation, LiDAR-inertial odometry, and a fused GPS/LIO trajectory. The dataset spans urban, suburban, and rural environments at sunrise through nighttime, and includes deliberately degraded-sensor recordings.

  • Curated by: Harpreet Sahota (FiftyOne multimodal packaging)
  • Original dataset by: Anthony Bisulco, Jeremy Wang, Kostas Daniilidis, Randall Balestriero, Pratik Chaudhari (GRASP Laboratory, University of Pennsylvania; Brown University)
  • Funded by: [More Information Needed]
  • Shared by: Harpreet Sahota
  • Language(s): Not applicable (sensor data)
  • License: MIT

Dataset Sources

Uses

Direct Use

  • Visual inspection and debugging of synchronized multi-sensor recordings in the FiftyOne multimodal viewer (Image, 3D, Map, Plot, and Logs tiles on a shared timeline).
  • Filtering episodes by platform, split, sensor coverage, geographic bounding box, speed, idle fraction, or ground-truth availability using the FiftyOne SDK and App sidebar.
  • Prototyping multi-modal perception pipelines against a known-good subset before scaling to the full 382-sequence release.
  • Evaluating sensor coverage and calibration quality across platforms before importing additional sequences.

Out-of-Scope Use

This FiftyOne package contains eight sequences (a curated subset). Training production models requires the full OctoSense release (anthonytec2/OctoSense). Radar range and bearing values use fitted scales (1/16 m per count, −1 bearing sign) derived from LiDAR cross-validation; they are not manufacturer-documented and should not be used as ground truth for radar calibration.

Dataset Structure

Overview

Media type: multimodal — one fo.Sample per episode, filepath pointing to a .mcap file. All sensor streams are encoded inside the MCAP with time-aligned messages on a shared nanosecond clock. The FiftyOne App renders them in synchronized Image, 3D, Map, Plot, and Logs tiles.

Sample count: 8 episodes (6 car, 1 boat, 1 Unitree quadruped)

Total recording time: ~46 minutes across all 8 episodes

Sample Fields

Field FiftyOne type Description
filepath StringField Absolute path to the episode's .mcap file
bag_id StringField ROS 2 bag identifier (e.g. rosbag2_2026_01_09-11_32_05)
session StringField Session label within the platform (e.g. sess8)
platform StringField Recording platform: car, boat, or unitree
start_time StringField Sequence wall-clock start time (ISO 8601)
split StringField train or test for car episodes; null for boat and unitree
is_daytime BooleanField Whether the sequence was recorded in daylight
degraded BooleanField Whether sensor degradation was logged for this sequence
has_seg BooleanField Whether pseudo-label semantic segmentation GT is available
duration_s FloatField Episode duration in seconds (from MCAP message timestamps)
message_count IntField Total MCAP messages across all channels
channel_count IntField Number of distinct MCAP channels
topics ListField(StringField) All MCAP topic strings in the episode
schemas ListField(StringField) All MCAP schema names (use for capability filtering)
n_lidar_frames IntField Number of LiDAR sweeps
n_rgb_frames IntField Number of RGB camera frames
n_imu_samples IntField Number of IMU samples
n_gps_fixes IntField GPS fixes logged (includes zero-fix rows on unitree)
n_gps_valid FloatField GPS fixes with valid lock
n_events_left IntField Total events from left event camera
n_events_right IntField Total events from right event camera
gps_quality StringField GPS fix quality string (e.g. single_m, rtk)
gps_lat_min FloatField Southern edge of episode geographic bounding box (degrees)
gps_lat_max FloatField Northern edge of episode geographic bounding box (degrees)
gps_lon_min FloatField Western edge of episode geographic bounding box (degrees)
gps_lon_max FloatField Eastern edge of episode geographic bounding box (degrees)
mean_speed_mph FloatField Mean driving speed in mph
idle_fraction FloatField Fraction of recording time at near-zero speed
distance_m FloatField Total distance driven in metres
rgb_cal_id StringField Camera calibration file identifier
imu_cal_id StringField IMU calibration file identifier
lidar_cal_id StringField LiDAR calibration file identifier
sensor_dropout StringField Dropout description if a sensor outage was logged; null otherwise

MCAP Stream Contents

Each .mcap file is a multi-channel recording. Topics and their schemas vary by platform:

Topic Schema Tile Platforms
/camera/left/image_raw foxglove.CompressedVideo (H.264, 10 fps) Image all
/camera/right/image_raw foxglove.CompressedVideo (H.264, 10 fps) Image all
/camera/infrared/image_raw foxglove.CompressedVideo (H.264, ~50 fps) Image all
/camera/left_rect/depth foxglove.RawImage (32fc1 metres, 10 Hz) Image car
/camera/left_rect/semantic foxglove.CompressedImage (PNG, 10 Hz) Image car daytime
/camera/left_rect/flow foxglove.CompressedVideo (H.264, 10 fps) Image car
/lidar/points foxglove.PointCloud (10 Hz) 3D all
/gps foxglove.LocationFix Map car, boat
/odom foxglove.PoseInFrame 3D car
/imu/vectornav imu_vectornav (JSON) Plot all
/imu/ouster imu_ouster (JSON) Plot all
/car/* JSON scalars (speed, wheels, steer, CAN) Plot car
/robot/* JSON scalars (low_state, sport, controller) Plot unitree
/captions foxglove.Log Logs car
/tf_static foxglove.FrameTransforms — all

Ground Truth Coverage

Ground truth Source Coverage Notes
Dense depth LiDAR accumulation, 32fc1 metres All 6 car episodes ~12 % of pixels valid; 3–102 m range
Semantic segmentation EoMT-Cityscapes-DINOv2-L-1024 pseudo-labels Daytime car only (all 5 daytime car episodes in this subset) 19 Cityscapes classes; 255 = ignore
Ego-motion optical flow Derived at conversion from depth + poses All 6 car episodes Ego-motion only; moving objects are holes
Radar tracks Mazda CAN track_{1..6} Car Range 1/16 m per count (fitted); bearing sign inverted

Filtering by Capability

Use the schemas list field to filter episodes rather than topic names (topic substrings false-positive against non-decodable packet channels):

import fiftyone as fo
from fiftyone import ViewField as F

dataset = fo.load_dataset("octosense")

# Car episodes with semantic segmentation
car_with_seg = dataset.match(F("has_seg") == True)

# Episodes with GPS
has_gps = dataset.match(F("schemas").contains("foxglove.LocationFix"))

# Filter by platform
cars = dataset.match(F("platform") == "car")

Dataset Creation

Curation Rationale

The eight episodes were selected to provide maximum coverage of platforms, modalities, and conditions within a manageable subset:

  • Daytime car with all GT (car/sess8, 53 s): shortest car sequence with depth, semantic segmentation, and all CAN channels present.
  • Car sessions (sess8 long, sess9, sess10 ×2, sess11): 163–597 s each, from both train and test splits, spanning all four car sessions; selected for near-zero sensor dropout, valid GPS lock, and minimal idle time.
  • Boat (boat/sess1, 54 s): the only platform where the LiDAR ships raw range images rather than XYZ, and where the boat IMU extrinsic differs.
  • Unitree (unitree/sess1, 112 s): the only platform with /robot joint state and no GPS or CAN data.

Source Data

Data Collection and Processing

Data was collected from a single OctoSense rig mounted on each platform. All sensors are hardware PPS-locked for temporal alignment and post-processed with Kalman-smoothed affine synchronization. Source recordings are ROS 2 bags converted to HDF5 and H.265 video files; the raw release is hosted at anthonytec2/OctoSense on Hugging Face (~8.5 TB). This FiftyOne package re-encodes video streams from HEVC to H.264 (libx264, no B-frames, one access unit per MCAP message) for browser WebCodecs compatibility and packages all channels into MCAP files using the Foxglove schema registry. LiDAR points are stored at a 20-byte stride (x, y, z, intensity as float32; r, g, b, a as uint8), with colour sampled from the nearest left-camera frame.

Who are the source data producers?

The OctoSense hardware platform and dataset were developed at the General, Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania (Anthony Bisulco, Jeremy Wang, Kostas Daniilidis, Pratik Chaudhari) and Brown University (Randall Balestriero). Data was collected on Long Island and in Philadelphia, PA, USA.

Annotations

Annotation process

All ground truth in OctoSense is machine-generated:

  • Depth is accumulated from LiDAR sweeps projected onto the rectified left camera frame.
  • Semantic segmentation is produced by EoMT-Cityscapes-DINOv2-L-1024 applied to CLAHE-enhanced rectified left frames; it is a model's output, not human annotation.
  • Optical flow is derived at MCAP-conversion time from depth and LiDAR-inertial poses (flow_gap = 2 frames, i.e. 0.2 s lookahead); it captures ego-motion only — pixels covering dynamic objects (vehicles, pedestrians) were excluded from the depth accumulation and appear as invalid.
  • Radar detection geometry (range, bearing) uses scales fitted against LiDAR-projected vehicle pixels in bird's-eye view (1/16 m per DIST_OBJ count, negative ANG_OBJ sign). These are not manufacturer-documented values.

Who are the annotators?

No human annotators. All labels are generated programmatically from sensor data and pre-trained models as described above.

Personal and Sensitive Information

Recordings were made on public roads and a private waterway. No personally identifiable information was intentionally captured. Faces and licence plates visible in RGB frames are not blurred in the source data.

Citation

BibTeX:

@article{bisulco2026octosense,
  title={OctoSense: Self-Supervised Learning for Multimodal Robot Perception},
  author={Bisulco, Anthony and Wang, Jeremy and Daniilidis, Kostas and Balestriero, Randall and Chaudhari, Pratik},
  journal={arXiv preprint arXiv:2606.27317},
  year={2026}
}

APA:

Bisulco, A., Wang, J., Daniilidis, K., Balestriero, R., & Chaudhari, P. (2026). OctoSense: Self-Supervised Learning for Multimodal Robot Perception. arXiv preprint arXiv:2606.27317.

More Information

Dataset Card Authors

Harpreet Sahota

Dataset Card Contact

harpreetsahota