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license: mit
---
# Mandeye Mapping – Loop Closure dataset
Handheld LiDAR + IMU + camera dataset recorded with a [Mandeye](https://github.com/JanuszBedkowski/mandeye_controller) scanner in an urban courtyard, with a loop in the trajectory. It comes with LiDAR-inertial odometry results from [HDMapping](https://github.com/MapsHD/HDMapping) (v0.104.0) and a LiDAR–camera calibration, so you can colorize the point cloud from the camera images.

▶️ Video: [https://youtu.be/jxjWX8dq30Y](https://youtu.be/jxjWX8dq30Y)
## Contents
| Path | Description |
|------|-------------|
| `lidar*.laz`, `lidar*.sn` | Raw LiDAR chunks (point clouds + serial numbers) |
| `imu*.csv` | IMU data (gyro, accelerometer, timestamps) |
| `status*.json` | Scanner status snapshots |
| `CAMERA_0_blurred/` | Camera images (`cam0_<timestamp>.jpg`), faces/plates blurred |
| `extrinsics.json` | Camera intrinsics (`fx, fy, cx, cy`, distortion) and `T_lidar_to_camera_4x4` extrinsics |
| `lio_result_0/` | HDMapping LiDAR-inertial odometry output: `scan_lio_*.laz`, `trajectory_lio_*.csv`, `session.mjs`, params `.toml` |
## Usage
1. Download the dataset:
```bash
git lfs install
git clone https://huggingface.co/datasets/MichalPelka/MandeyeMappingLoopClosure
```
2. Get [HDMapping](https://github.com/MapsHD/HDMapping) ([releases](https://github.com/MapsHD/HDMapping/releases)).
3. (Optional) Re-run LiDAR odometry on the raw data with HDMapping's `lidar_odometry_step_1`, or use the provided `lio_result_0/`.
4. Open the **Trajectory Viewer** and in the side panel:
- **Session** → *LIO result directory*: `./lio_result_0`, leave *CAMERA_0 directory* empty (auto-detects `CAMERA_0_blurred`) → **Load session** → **Load cloud**
- **Calibration** → *Calibration JSON*: `extrinsics.json` → **Load calibration**
- **Export** → **Export colored LAZ** / **Export colored E57**
## Links
- Video: https://youtu.be/jxjWX8dq30Y
- HDMapping: https://github.com/MapsHD/HDMapping
- Mandeye controller: https://github.com/JanuszBedkowski/mandeye_controller
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