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

Ran Duan, Yurong Feng and Chih-Yung Wen ()
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Ran Duan: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Yurong Feng: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Chih-Yung Wen: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China

Sustainability, 2022, vol. 14, issue 19, 1-11

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.

Keywords: pose graph; loop closure detection; semantic VSLAM; deep feature matching (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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