Semantic Graph Based Place Recognition for 3D Point Clouds

TL;DR

This paper introduces a semantic graph-based method for 3D point cloud place recognition that is robust to occlusion and viewpoint changes, outperforming existing techniques on the KITTI dataset.

Contribution

It presents a novel semantic graph representation and a graph similarity network for robust place recognition in 3D point clouds.

Findings

Outperforms state-of-the-art methods on KITTI dataset

Robust to occlusion and viewpoint variations

Effective semantic graph matching approach

Abstract

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}.