LiDARNet v1

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. We developed a semi-automatic 3D object labeling tool to store individual object point clouds. 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.

 

 

LiDAR:

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. Wei Song*, Dechao Li, Su Sun, Xinghui Xu and Guidong Zu*, Registration for 3-D LiDAR Datasets using Pyramid Reference Object, IEEE Transactions on Instrumentation and Measurement, doi: 10.1109/TIM.2023.3300410, 2023.08.
2. Wei Song, Zhen Liu, Ying Guo, Su Sun, Guidong Zu, and Maozhen Li, DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV, Remote Sensing, Vol.14, No.13: 3825. 2022, https://doi.org/10.3390/rs14153825
3. Wei Song, Dechao Li, Su Sun, Lingfeng Zhang, Yu Xin, Yunsick Sung, and Ryong Choi, 2D&3DHNet for 3D Object Classification in LiDAR Point Cloud, Remote Sensing, Vol.14, No.13: 3146. 2022, https://doi.org/10.3390/rs14133146

 

Principal Investigator

Wei Song ([email protected])