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README.md

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 ---
-license: cc-by-nc-4.0
+license: cc-by-4.0
+task_categories:
+- robotics
+tags:
+- robotics
+- lidar
+- traversability
+- off-road
+- construction
+- point-cloud
+- autonomous-navigation
+size_categories:
+- 10K<n<100K
 ---
+
+# Construction-Site Multimodal Dataset (CMAD)
+
+Real-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.
+
+This 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.
+
+## Sensor rig
+
+- RGB camera, 640×480, intrinsics `fx≈399.7, fy≈399.8, cx≈330.4, cy≈197.7`.
+- Solid-state LiDAR, extrinsically calibrated against the rig's static TF tree.
+
+## Dataset structure
+
+| Sequence | Frames | Duration | Notes |
+|---|---|---|---|
+| `00000` | 9,767 | ≈47.0 min | |
+| `00001` | 3,100 | ≈14.6 min | |
+| `00002` | 3,534 | ≈16.5 min | odometry diverges in the final ~3% of frames — see Known Issues below |
+| **Total** | **16,401** | **≈78.1 min** | ≈3.5 Hz camera/LiDAR rate |
+
+Each sequence directory contains:
+
+```
+<seq>/
+├── calib.txt
+├── camera_info.txt
+├── camera_times.txt
+├── frame_times.txt
+├── poses.txt                      # per-frame 6-DoF pose, row-major flattened 3x4 [R|t]
+├── imu.npy
+├── sync_report.csv
+├── pylon_camera_node/              # RGB images, one per frame
+└── os1_cloud_node_kitti_bin/       # LiDAR point clouds, one per frame (KITTI .bin format)
+```
+
+## Known issues
+
+Sequence `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.
+
+## License
+
+This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
+
+The 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.