Datasets:

Voxel51
/

OctoSense

---
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.gif)

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

```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 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

- **Repository:** https://huggingface.co/datasets/anthonytec2/OctoSense
- **Paper:** https://arxiv.org/abs/2606.27317
- **Demo:** https://abisulco.com/octosense/

## 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):

```python
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:**

```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

- Full dataset (8.5 TB, 382 sequences): https://huggingface.co/datasets/anthonytec2/OctoSense
- Devkit and conversion scripts: https://github.com/anthonytec2/OctoSense
- Project page and supplementary videos: https://abisulco.com/octosense/
- FiftyOne multimodal documentation: https://docs.voxel51.com/user_guide/app.html#multimodal-viewer
## Dataset Card Authors
Harpreet Sahota
## Dataset Card Contact
harpreetsahota

	---
	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.gif)

	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

	```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 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

	- Repository: https://huggingface.co/datasets/anthonytec2/OctoSense
	- Paper: https://arxiv.org/abs/2606.27317
	- Demo: https://abisulco.com/octosense/

	## 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):

	```python
	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:

	```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

	- Full dataset (8.5 TB, 382 sequences): https://huggingface.co/datasets/anthonytec2/OctoSense
	- Devkit and conversion scripts: https://github.com/anthonytec2/OctoSense
	- Project page and supplementary videos: https://abisulco.com/octosense/
	- FiftyOne multimodal documentation: https://docs.voxel51.com/user_guide/app.html#multimodal-viewer

	## Dataset Card Authors

	Harpreet Sahota

	## Dataset Card Contact

	harpreetsahota