README.md

---
pretty_name: Odometry ROSBag Dataset
license: cc-by-nc-4.0
task_categories:
  - robotics
tags:
  - rosbag
  - odometry
  - robotics
  - imu
  - lidar
  - camera
  - tof
  - timeseries
  - pandas
size_categories:
  - n<1K
configs:
  - config_name: sessions
    data_files: metadata/sessions.parquet
  - config_name: topics
    data_files: metadata/topics.parquet
---

<p align="center">
  <img src="docs/assets/readme/odometry_dataset_hero.png" alt="Odometry ROSBag Dataset overview" width="100%">
</p>

# Odometry ROSBag Dataset

A multi-sensor ROS1 dataset for odometry research, ToF sensing, inertial analysis, and reproducible Foxglove visualization.

<p align="center">
  <a href="https://huggingface.co/datasets/ly041021/odom_dataset/blob/main/README.zh-CN.md"><b>简体中文</b></a> ·
  <a href="https://github.com/Ly041021/odom_dataset"><b>GitHub Repository</b></a> ·
  <a href="#foxglove-visualization-sample"><b>Foxglove Sample</b></a> ·
  <a href="#use-this-dataset"><b>Download Guide</b></a> ·
  <a href="#sensor-coverage"><b>Sensor Coverage</b></a> ·
  <a href="#citation"><b>Citation</b></a>
</p>

Raw ROSBag files are the primary data source. Lightweight Parquet and YAML indexes provide session-level and topic-level summaries so users can inspect the dataset before downloading large bags. Code, import scripts, validation scripts, tests, and extended documentation are maintained in the GitHub repository: https://github.com/Ly041021/odom_dataset

<p align="center">
  <img src="docs/assets/readme/dataset_workflow_overview.png" alt="Dataset workflow overview" width="92%">
</p>

## Highlights

| Capability | Included assets |
| --- | --- |
| Multi-modal robotic sensing | ToF, flight-controller IMU, LiDAR IMU, MID360 LiDAR, RealSense RGB/depth/infrared camera, odometry, and TF streams |
| Raw-data preservation | Original ROS1 `bag.bag` files are retained as the source of truth |
| Lightweight discovery | Parquet/YAML indexes summarize sessions, topics, duration, coverage, and quality before raw-data download |
| Reproducible visualization | A Foxglove sample bag and a conversion script are provided for compact inspection workflows |
| Sensor documentation | Hardware identities, ToF operating mode, calibration status, and quality reports are available in structured files |

## Quick Facts

| Item | Value |
| --- | --- |
| Sessions | 52 |
| Total duration | 12,125.257 sec (202.09 min) |
| Raw ROSBag size | 301.20 GiB |
| Session index rows | 52 |
| Topic index rows | 663 |
| Sessions with all indexed sensor groups | 26/52 |
| Raw format | ROS1 `bag.bag` files |
| Timestamp unit | nanoseconds (`ns`) |
| Ready-to-open Foxglove sample | `examples/foxglove/visual_demo.bag` |


## Use This Dataset

Download the lightweight indexes first. Avoid cloning the full dataset unless you need all raw ROSBag files.

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ly041021/odom_dataset",
    repo_type="dataset",
    allow_patterns=["metadata/**", "quality/**", "calibration/**", "dataset.yaml"],
    local_dir="odom_dataset_index",
)
```

Load the indexes with pandas:

```python
import pandas as pd

sessions = pd.read_parquet("metadata/sessions.parquet")
topics = pd.read_parquet("metadata/topics.parquet")
```

Download one raw session when you know which bag you need:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ly041021/odom_dataset",
    repo_type="dataset",
    allow_patterns=["raw/sessions/2026-05-20_030414_odom_run029/**"],
    local_dir="odom_dataset_raw",
)
```

Download the ready-to-open Foxglove sample:

```python
from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="ly041021/odom_dataset",
    repo_type="dataset",
    filename="examples/foxglove/visual_demo.bag",
    local_dir="odom_dataset_visualization",
)
```

## Foxglove Visualization Sample

The dataset includes a ready-to-open Foxglove visualization sample at `examples/foxglove/visual_demo.bag`. It exposes synchronized RGB, LiDAR, ToF, IMU, and odometry views without requiring users to process a raw bag first.

<p align="center">
  <img src="docs/assets/readme/foxglove_all_modalities.gif" alt="Foxglove multi-modal visualization" width="96%">
</p>

| RGB / Depth | LiDAR |
| --- | --- |
| <img src="docs/assets/readme/foxglove_rgb_depth.gif" alt="Foxglove RGB and depth visualization" width="100%"> | <img src="docs/assets/readme/foxglove_lidar.gif" alt="Foxglove LiDAR point cloud visualization" width="100%"> |

| IMU Plot | Odometry Path |
| --- | --- |
| <img src="docs/assets/readme/foxglove_imu_plot.gif" alt="Foxglove MAVROS IMU plot visualization" width="100%"> | <img src="docs/assets/readme/foxglove_odometry_path.gif" alt="Foxglove odometry path visualization" width="100%"> |

| ToF Overview |
| --- |
| <img src="docs/assets/readme/foxglove_tof.gif" alt="Foxglove TOFSense-M overview visualization" width="100%"> |

Recommended 3D panel setup: `Fixed frame = odom`, `Display frame = odom`.

## Data Layout

```text
raw/sessions/<session_id>/bag.bag              # raw ROS1 bag
raw/sessions/<session_id>/bag.bag.sha256       # checksum
raw/sessions/<session_id>/metadata.yaml        # session metadata
raw/sessions/<session_id>/topic_summary.yaml   # per-topic statistics
metadata/sessions.parquet                      # session index
metadata/topics.parquet                        # topic index
metadata/tof_sensor.yaml                       # TOFSense-M cascade metadata
quality/reports/                               # generated quality reports
calibration/robot_v1_template/                 # calibration files
docs/foxglove_visualization.md                 # Foxglove visualization guide
scripts/foxglove_visual.py                     # Foxglove visualization bag builder
examples/foxglove/visual_demo.bag              # ready-to-open Foxglove sample
```

## Sensor Coverage

| Sensor group | Session coverage | Notes |
| --- | ---: | --- |
| LiDAR | 44/52 | `/livox/lidar` |
| RGB camera | 28/52 | `/camera/color/camera_info`, `/camera/color/image_raw` |
| Depth camera | 35/52 | `/camera/depth/camera_info`, `/camera/depth/image_rect_raw` |
| Infrared camera | 35/52 | `/camera/infra1/camera_info`, `/camera/infra1/image_rect_raw`, `/camera/infra2/camera_info`, `/camera/infra2/image_rect_raw` |
| ToF | 50/52 | `/nlink_tofsensem_cascade` |
| IMU | 52/52 | `/livox/imu`, `/mavros/imu/data`, `/mavros/imu/data_raw` |
| Odometry | 52/52 | `/ekf_quat/ekf_odom`, `/fusion_odometry/lazy_point_odom` |
| TF | 52/52 | `/tf_static` |


## Observed Topics

| Category | Topic | ROS message type | Sessions | Messages | Median Hz |
| --- | --- | --- | ---: | ---: | ---: |
| imu | `/livox/imu` | `sensor_msgs/Imu` | 44 | 1,977,290 | 200.00 |
| imu | `/mavros/imu/data` | `sensor_msgs/Imu` | 52 | 2,417,806 | 199.52 |
| imu | `/mavros/imu/data_raw` | `sensor_msgs/Imu` | 52 | 2,420,793 | 199.79 |
| lidar | `/livox/lidar` | `livox_ros_driver2/CustomMsg` | 44 | 99,312 | 10.00 |
| tof | `/nlink_tofsensem_cascade` | `nlink_parser/TofsenseMCascade` | 50 | 179,314 | 14.95 |
| camera | `/camera/color/camera_info` | `sensor_msgs/CameraInfo` | 28 | 164,928 | 29.98 |
| camera | `/camera/color/image_raw` | `sensor_msgs/Image` | 28 | 164,925 | 29.98 |
| camera | `/camera/depth/camera_info` | `sensor_msgs/CameraInfo` | 35 | 103,077 | 15.00 |
| camera | `/camera/depth/image_rect_raw` | `sensor_msgs/Image` | 35 | 103,078 | 15.00 |
| camera | `/camera/infra1/camera_info` | `sensor_msgs/CameraInfo` | 35 | 103,073 | 15.00 |
| camera | `/camera/infra1/image_rect_raw` | `sensor_msgs/Image` | 35 | 103,073 | 15.00 |
| camera | `/camera/infra2/camera_info` | `sensor_msgs/CameraInfo` | 35 | 103,072 | 15.00 |
| camera | `/camera/infra2/image_rect_raw` | `sensor_msgs/Image` | 35 | 103,072 | 15.00 |
| odometry | `/ekf_quat/ekf_odom` | `nav_msgs/Odometry` | 52 | 2,395,361 | 199.31 |
| odometry | `/fusion_odometry/lazy_point_odom` | `nav_msgs/Odometry` | 51 | 2,294,922 | 199.29 |
| tf | `/tf_static` | `tf2_msgs/TFMessage` | 52 | 87 | 1075.35 |


## Quality Notes

- Sessions missing RGB color topics: 24/52.
- Sessions missing ToF topic: 2/52.
- ToF sensor metadata: six-node Nooploop TOFSense-M cascade, UART query mode, 8x8 pixels per node. See `metadata/tof_sensor.yaml` and `metadata/tof_sensor.md`.
- ToF validity report: 52,208,469/67,587,392 valid pixels (77.25%).
- `quality/reports/topic_completeness_and_tof_validity.md` contains the detailed per-session report.
- `calibration/robot_v1_template/` contains MAVROS IMU, MID360 LiDAR, D430 infrared stereo camera calibration, and the explicit ToF extrinsic status. Use the recorded `/camera/*/camera_info` topics for RealSense color/depth intrinsics. Fixed ToF extrinsic is not available in this dataset version.

## Build Custom Visualization Bags

`scripts/foxglove_visual.py` creates Foxglove-ready visualization bags with compressed TOFSense-M overview images, optional RGB topics, standard `sensor_msgs/PointCloud2` output at `/foxglove/livox/points`, accumulated `nav_msgs/Path` output at `/foxglove/odom/path`, MAVROS IMU topics, and odometry TF. In Foxglove, use `Fixed frame = odom` and `Display frame = odom` for the 3D panel. See `docs/foxglove_visualization.md` for command-line examples and panel setup.

## Limitations

- No fixed train/validation/test split or benchmark protocol is defined in this release.
- Images, depth frames, infrared frames, lidar point clouds, and odometry streams are not exported as separate training files.
- Topic scanning and raw bag decoding require a ROS1 environment with the `rosbag` Python package.
- Raw bags are large. Start from the Parquet indexes, then download only the sessions you need.

## Citation

```bibtex
@dataset{odometry_rosbag_dataset,
  title = {Odometry ROSBag Dataset},
  year = {2026},
  note = {Raw ROSBag storage dataset with metadata indexes}
}
```

## License

Dataset files are licensed under CC BY-NC 4.0. Commercial use is not permitted under this dataset license. See `DATA_LICENSE.md` for the repository-specific data license note. Source code and documentation tooling in the GitHub repository are licensed separately under MIT.

---

This card is generated from `metadata/sessions.parquet`, `metadata/topics.parquet`, and `quality/reports/`.