3D LiDAR-based place recognition remains challenging due to ambiguities and dynamics of scenes. Place representation can be learned effectively by deep learning, but perceptual confusion is inevitable based on single-scan. To alleviate the problem, we propose a new place recognition method named SC-LPR. In this method, spatiotemporal contextual information from LiDAR scans is used to increase the capacity of features' representation. Firstly, a semantic graph is constructed to represent the topological geometric map of each LiDAR scan. Then, an end-to-end network is designed to predict similarity, in which GRU-EdgeConv++ is proposed to learn discriminative spatiotemporal feature representation and a novel C TNT to predict a score. We evaluate our approach on the KITTI odometry benchmark. The experimental results show that our method can effectively fuse spatiotemporal information for place recognition. Compared with the state-of-the-art methods, our approach is superior on the KITTI dataset and can achieve competitive performance. To benefit the community by serving as a benchmark for place recognition, the code of our method will be made open-source on Github(1). (C) nbsp;2022 Elsevier B.V. All rights reserved.
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