NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection
- Resource Type
- Conference
- Authors
- Zhou, Ruihao; He, Li; Zhang, Hong; Lin, Xubin; Guan, Yisheng
- Source
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on. :1328-1335 Oct, 2022
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Point cloud compression
Correlation coefficient
Three-dimensional displays
Laser radar
Shape
Navigation
Source coding
- Language
- ISSN
- 2153-0866
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.