Deep Pose Graph-Matching-Based Loop Closure Detection for Semantic Visual SLAM

Abstract

This work addresses the loop closure detection issue by matching the local pose graphs for semantic visual SLAM. We propose a deep feature matching-based keyframe retrieval approach. The proposed method treats the local navigational maps as images. Thus, the keyframes may be considered keypoints of the map image. The descriptors of the keyframes are extracted using a convolutional neural network. As a result, we convert the loop closure detection problem to a feature matching problem so that we can solve the keyframe retrieval and pose graph matching concurrently. This process in our work is carried out by modified deep feature matching (DFM). The experimental results on the KITTI and Oxford RobotCar benchmarks show the feasibility and capabilities of accurate loop closure detection and the potential to extend to multiagent applications.