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• W4296875152 endingPage "11864" @default.
• W4296875152 startingPage "11864" @default.
• W4296875152 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." @default.
• W4296875152 created "2022-09-24" @default.
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• W4296875152 creator A5047882981 @default.
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• W4296875152 date "2022-09-21" @default.
• W4296875152 modified "2023-10-18" @default.
• W4296875152 title "Deep Pose Graph-Matching-Based Loop Closure Detection for Semantic Visual SLAM" @default.
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• W4296875152 doi "https://doi.org/10.3390/su141911864" @default.
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