Datasets:
File size: 2,877 Bytes
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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.
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