  Place recognition and loop-closure detection are main challenges in the
localization, mapping and navigation tasks of self-driving vehicles. In this
paper, we solve the loop-closure detection problem by incorporating the
deep-learning based point cloud description method and the coarse-to-fine
sequence matching strategy. More specifically, we propose a deep neural network
to extract a global descriptor from the original large-scale 3D point cloud,
then based on which, a typical place analysis approach is presented to
investigate the feature space distribution of the global descriptors and select
several super keyframes. Finally, a coarse-to-fine strategy, which includes a
super keyframe based coarse matching stage and a local sequence matching stage,
is presented to ensure the loop-closure detection accuracy and real-time
performance simultaneously. Thanks to the sequence matching operation, the
proposed approach obtains an improvement against the existing deep-learning
based methods. Experiment results on a self-driving vehicle validate the
effectiveness of the proposed loop-closure detection algorithm.
