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Loop closure detection is a key technology for long-term robot navigation incomplex environments. In this paper, we present a global descriptor, namedNormal Distribution Descriptor (NDD), for 3D point cloud loop closuredetection. The descriptor encodes both the probability density score andentropy of a point cloud as the descriptor. We also propose a fast rotationalignment process and use correlation coefficient as the similarity betweendescriptors. Experimental results show that our approach outperforms thestate-of-the-art point cloud descriptors in both accuracy and efficency. Thesource code is available and can be integrated into existing LiDAR odometry andmapping (LOAM) systems.

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