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arXiv · 2209.12513

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

Ruihao Zhou·Li He·Hong Zhang·Xubin Lin·Yisheng Guan
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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.

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Ruihao Zhou, Li He, Hong Zhang, Xubin Lin, Yisheng Guan. 2022-09-26. NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection. https://arxiv.org/abs/2209.12513

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