Semantic Graph Based Place Recognition for 3D Point Clouds
This work addresses place recognition for robotics and autonomous systems, offering a novel method that improves robustness in dynamic environments, though it is incremental in its application of semantic graphs to an existing problem.
The paper tackles the challenge of place recognition in 3D point clouds by proposing a semantic graph-based approach that models recognition as a graph matching problem, achieving robust performance against occlusion and viewpoint changes and outperforming state-of-the-art methods on the KITTI dataset.
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: \url{https://github.com/kxhit/SG_PR}.