LuSNAR
The LuSNAR dataset, created by the Institute of Space Application Engineering and Technology and the Key Laboratory of Space Utilization of the Chinese Academy of Sciences, is a multi‑task, multi‑scenario lunar surface dataset. It contains nine Unreal Engine‑based simulated lunar scenes, each divided by terrain roughness and object density, and provides high‑resolution stereo image pairs, panoramic semantic labels, depth maps, and point clouds. The dataset is intended to support development and validation of perception and navigation algorithms, suitable for 2‑D/3‑D semantic segmentation, visual SLAM, LiDAR SLAM, stereo matching, and 3‑D reconstruction, focusing on autonomous perception and navigation for lunar exploration.
Dataset description and usage context
LuSNAR Dataset
Introduction
LuSNAR is a multi‑sensor (stereo camera, LiDAR, IMU) lunar segmentation, navigation, and reconstruction dataset for autonomous exploration. It comprises 9 Unreal Engine‑based simulated lunar scenes, each partitioned by terrain undulation and object density.
Data Content
The dataset includes:
- High‑resolution stereo image pairs
- Panoramic semantic labels
- Dense depth maps
- LiDAR point clouds
- IMU data
- Rover pose data
Application Scenarios
Applicable for comprehensive evaluation of autonomous perception and navigation systems, including:
- 2D/3D semantic segmentation
- Visual/LiDAR SLAM
- 3D reconstruction
Availability
Total size 108 GB, consisting of:
- 42 GB stereo image pairs
- 50 GB depth maps
- 356 MB semantic segmentation labels
- 14 GB single‑frame point clouds (with semantics)
Dataset structure:
├── image1
│ ├── RGB
│ │ ├── timestamp1.png
│ │ ├── timestamp2.png
│ │ └── ...
│ ├── Depth
│ │ ├── timestamp1.png
│ │ ├── timestamp2.png
│ │ └── ...
│ └── Label
│ ├── timestamp1.png
│ ├── timestamp2.png
│ └── ...
├── image2
│ ...
├── LiDAR
│ ├── timestamp1.txt
│ ├── timestamp2.txt
│ └── ...
├── Rover_pose.txt
└── IMU.txt
Semantic image color‑to‑class mapping:
| Class ID | Class | Color |
|---|---|---|
| 0 | Lunar Regolith | BB469C |
| 1 | Crater | 7800C8 |
| 2 | Rock | E8FA50 |
| 3 | Mountain | AD451F |
| 4 | Sky | 22C9F8 |
LiDAR point‑cloud class mapping:
| Class ID | Class |
|---|---|
| -1 | Lunar Regolith |
| 0 | Crater |
| 174 | Rock |
File Formats
LiDAR/timestamp.txt
| x [m] | y [m] | z [m] | Class ID |
Rover_pose.txt
| Timestamp [ns] | p_RS_R_x [m] | p_RS_R_y [m] | p_RS_R_z [m] | q_RS_w [] | q_RS_x [] | q_RS_y [] | q_RS_z [] | v_RS_R_x [m s^-1] | v_RS_R_y [m s^-1] | v_RS_R_z [m s^-1] | b_w_RS_S_x [rad s^-1] | b_w_RS_S_y [rad s^-1] | b_w_RS_S_z [rad s^-1] | b_a_RS_S_x [m s^-2] | b_a_RS_S_y [m s^-2] | b_a_RS_S_z [m s^-2] |
IMU.txt
| Timestamp [ns] | w_RS_S_x [rad s^-1] | w_RS_S_y [rad s^-1] | w_RS_S_z [rad s^-1] | a_RS_S_x [m s^-2] | a_RS_S_y [m s^-2] | a_RS_S_z [m s^-2] |
Pair the dataset with AI analysis and content workflows.
Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.