Indoor Loop Closure Detection Based on Semantic Topology Graph Matching

Abstract

Loop closure detection is a fundamental problem for visual simultaneous localization and mapping (VSLAM) in robotics. However, the current loop closure detection is mostly based on pixel-level recognition and matching algorithms which often fail under drastic viewpoint changes and illumination variations. This work is based on the idea that topological graph representation has better abstraction and globality for indoor scene. Based on this knowledge, we propose a method that pays more attention to scene global information to model visual scenes as semantic topological graphs by preserving only semantic information from object detection and geometric information from RGB-D cameras. We use the random walk method to traverse the graph structure to construct the graph descriptor implementing graph matching. Furthermore, the shape similarity and the Euclidean distance between objects in the 3D space are leveraged unitedly to measure the graph similarity. Comparing our method with existing classical methods in TUM dataset and indoor realistic complex scenes, the results show that our method has good performance compared to appearance-based and semantic-based methods.