  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.
