SeqLPD: Sequence Matching Enhanced LoopClosure Detection Based on LargeScale Point Cloud Description for SelfDriving Vehicles Place recognition and loopclosure detection are main challenges in the localization, mapping and navigation tasks of selfdriving vehicles. In this paper, we solve the loopclosure detection problem by incorporating the deeplearning based point cloud description method and the coarsetofine sequence matching strategy. More specifically, we propose a deep neural network to extract a global descriptor from the original largescale 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 coarsetofine strategy, which includes a super keyframe based coarse matching stage and a local sequence matching stage, is presented to ensure the loopclosure detection accuracy and realtime performance simultaneously. Thanks to the sequence matching operation, the proposed approach obtains an improvement against the existing deeplearning based methods. Experiment results on a selfdriving vehicle validate the effectiveness of the proposed loopclosure detection algorithm.

Robust Loop Closure Detection based on Bag of SuperPoints and Graph Verification Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature descriptors using the SuperPoint neural network. Then a visual vocabulary is trained in an incremental and selfsupervised manner considering the relations between consecutive training images. Finally, a topological graph is constructed using matched feature points to verify candidate loop closures obtained by a BagofWords (BoW) framework. Comparative experiments with stateoftheart LCD algorithms on several typical datasets have been carried out. The results demonstrate that our proposed graph verification method can significantly improve the accuracy of image matching and the overall LCD approach outperforms existing methods.