LiDAR Scanning Outdoor Object Dataset (LSOOD)

This dataset is collected by an HDL-32E Velodyne LiDAR sensor carried by our UGV platform. Raw point clouds collected from a real outdoor scene are segmented into individual obstacles according to a fast spatial clustering method [1]. We developed a semi-automatic 3D object labeling tool to store individual object point clouds [2]. The UGV and a semi-automatic 3D object labeling tool are presented in the following figure.

 

 

 

We collected 1056 obstacles from several thousands of scanning frames, containing 4 kinds of common types. All the point coordinates are stored in .csv files with their original and after-centralized x, y, z coordinates.

 

 

LSOOD:

Train&Testing sample statistic

	Building	Bush	Pedestrian	Tree	Total
Train	180	110	50	190	530
Test	155	113	33	225	526
Total	335	223	83	415	1056

 

Citation

If you find our work useful in your research, please consider citing:

1. Y Tian, W Song, L Chen, et al., A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming, Sensors 20 (8), 2309
2. W Song, L Zhang, Y Tian, et al., CNN-based 3D object classification using Hough space of LiDAR point clouds, Human-centric Computing and Information Sciences 10 (1), 1-14

 

Principal Investigator

Tian Yifei ([email protected]); Song Wei ([email protected])

 

Project Researchers

Zhang Lifeng; Liu Zishu