NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection

Ruihao Zhou1; Li He2; Hong Zhang2; Xubin Lin1; Yisheng Guan1

10.1109/IROS47612.2022.9981180

2022

2153-0858

978-1-6654-7928-8

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

1328-1335

23-27 Oct. 2022

Kyoto, Japan

Loop closure detection is a key technology for long-term robot navigation in complex environments. In this paper, we present a global descriptor, named Normal Distribution Descriptor (NDD), for 3D point cloud loop closure detection. The descriptor encodes both the probability density score and entropy of a point cloud as the descriptor. We also propose a fast rotation alignment process and use correlation coefficient as the similarity between descriptors. Experimental results show that our approach outperforms the state-of-the-art point cloud descriptors in both accuracy and efficency. The source code is available and can be integrated into existing LiDAR odometry and mapping (LOAM) systems.

Point cloud compression Correlation coefficient Three-dimensional displays Laser radar Shape Navigation Source coding

其他

IEEE

1.Ruihao Zhou, Xubin Lin and Yisheng Guan is with the Department of Electromechanical Engineering, Guangdong University of technology, China
2.Department of Electronic and Electrical Engineering, Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, China

Ruihao Zhou,Li He,Hong Zhang,et al. NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection[C],2022:1328-1335.