vla-model/construction-syn-data

1---2license: cc-by-4.03task_categories:4- robotics5tags:6- robotics7- lidar8- traversability9- off-road10- construction11- point-cloud12- autonomous-navigation13size_categories:14- 10K<n<100K15---16 17# Construction-Site Multimodal Dataset (CMAD)18 19Real-world RGB + LiDAR + pose data collected on a live construction site with a custom ground-robot sensor rig. Three sequences, **16,401 synchronized frames (~78 minutes)** of camera, LiDAR, and 6-DoF odometry, laid out in a directory/loader convention compatible with the public [RELLIS-3D](https://unmannedlab.github.io/research/RELLIS-3D) benchmark, so existing off-road traversability-estimation tooling built around that convention can consume it with only a root-path change.20 21This dataset was collected to support research on self-supervised traversability estimation in tight, unstructured, real-world spaces — construction sites in particular, where narrow corridors, active equipment, and LiDAR-hazardous surfaces (open trenches, standing water, reflective debris) are common. Collection intentionally includes routes an operator judged difficult or borderline-traversable, not only easy/typical driving.22 23## Sensor rig24 25- RGB camera, 640×480, intrinsics `fx≈399.7, fy≈399.8, cx≈330.4, cy≈197.7`.26- Solid-state LiDAR, extrinsically calibrated against the rig's static TF tree.27 28## Dataset structure29 30| Sequence | Frames | Duration | Notes |31|---|---|---|---|32| `00000` | 9,767 | ≈47.0 min | |33| `00001` | 3,100 | ≈14.6 min | |34| `00002` | 3,534 | ≈16.5 min | odometry diverges in the final ~3% of frames — see Known Issues below |35| **Total** | **16,401** | **≈78.1 min** | ≈3.5 Hz camera/LiDAR rate |36 37Each sequence directory contains:38 39```40<seq>/41├── calib.txt42├── camera_info.txt43├── camera_times.txt44├── frame_times.txt45├── poses.txt                      # per-frame 6-DoF pose, row-major flattened 3x4 [R|t]46├── imu.npy47├── sync_report.csv48├── pylon_camera_node/              # RGB images, one per frame49└── os1_cloud_node_kitti_bin/       # LiDAR point clouds, one per frame (KITTI .bin format)50```51 52## Known issues53 54Sequence `00002`'s onboard LiDAR odometry is reliable for the great majority of the sequence but diverges sharply in its final ~3% of frames (single-frame position jumps of tens of meters, consistent with an odometry tracking failure). Reported here transparently rather than filtered out — if using pose data from this sequence, exclude the tail or verify pose continuity before relying on it.55 56## License57 58This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.59 60The recordings may be used, shared, and adapted for non-commercial purposes with appropriate attribution. Commercial use is not permitted without separate permission from the dataset authors.61