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:

LiDAR point‑cloud class mapping:

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] |