Semantic Graph Based Place Recognition for 3D Point Clouds

An illustration of place recognition. This is a reverse loop closure in sequence 08 of KITTI detected by our proposed approach. Note that the heading direction of frame 714 and 1499 are almost exactly the opposite, which brings a challenge to existing methods.


Due to the difficulty in generating the effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most of the existing methods that focus on extracting local, global, and statistical features of raw point clouds, our method aims at the semantic level that can be superior in terms of robustness to environmental changes. Inspired by the perspective of humans, who recognize scenes through identifying semantic objects and capturing their relations, this paper presents a novel semantic graph based approach for place recognition. First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud. Thus, place recognition is modeled as a graph matching problem. Then we design a fast and effective graph similarity network to compute the similarity. Exhaustive evaluations on the KITTI dataset show that our approach is robust to the occlusion as well as viewpoint changes and outperforms the state-of-the-art methods with a large margin. Our code is available at:

Published in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Guangyao Zhai
Guangyao Zhai