SSGM: Spatial Semantic Graph Matching for Loop Closure Detection in Indoor Environments

Capturing the semantics of objects and the topological relationship allows the robot to describe the scene more intelligently like a human and measure the similarity between scenes (loop closure detection) more accurately. However, many current semantic graph matching methods are based on walk descriptors, which only extract adjacency relations between objects. In such way, the comprehensive information in the semantic graph is not fully exploited, which may lead to false closed-loop detection. This paper proposes a novel spatial semantic graph matching method (SSGM) in indoor environments, which considers multifaceted information of the semantic graphs. Firstly, two semantic graphs are aligned in the same coordinate space contributed by the second-order spatial compatibility metric between objects and local graph features of objects in semantic graphs. Secondly, the similarity of the spatial distribution of overall semantic graphs is further evaluated. The proposed algorithm is validated on public datasets and compared with the latest semantic graph matching methods, demonstrating improved accuracy and efficiency in loop closure detection. The code is available at https://github.com/BIT-TYJ/SSGM.