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.
@article{arxiv.2209.12513,
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},
journal= {arXiv preprint arXiv:2209.12513},
year = {2022}
}