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

Ruihao Zhou; Li He; Hong Zhang; Xubin Lin; Yisheng Guan

IROS 2022poster14 citations

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

Ruihao Zhou, Li He, Hong Zhang, Xubin Lin, Yisheng Guan

Abstract

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.

BibTeX

@inproceedings{iros2022_ndda3dpointcloud,
  title = {NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection},
  author = {Ruihao Zhou and Li He and Hong Zhang and Xubin Lin and Yisheng Guan},
  booktitle = {IROS 2022},
  year = {2022}
}