Robust Visual Place Recognition Based on Context Information. Issue 22 (2019)

• Record Type: Journal Article
• Authors: Dai, Deyun; Chen, Zonghai; Wang, Jikai; Bao, Peng; Zhao, Hao
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 22(2019)
• Page Start: 49
• Page End: 54
• Publication Date: 2019
• Subjects: place recognition -- CNN features -- weighted DAG -- Dijkstra algorithm; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.11.046
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In large-scale and long-term visual SLAM, robust place recognition is essential for building a global consistent map. However, sensor viewpoints and environmental condition changes, including lighting, weather, and seasons, bring a huge challenge to place recognition. We propose a place recognition algorithm based on CNN features and graph model. Firstly, CNN features of images are extracted though an AlexNet network with migration characteristics, and N-nearest neighbor image descriptors of the current image descriptor are found by approximate nearest neighbor searching. Then, according to the difference between descriptors, a weighted directed acyclic graph (weighted DAG) model which describes a cost of context matching between images is established. Finally, a candidate matching sequence with minimum cost on this model is achieved by using Dijkstra algorithm. Compared with SeqCNNSLAM and Fast-SeqSLAM, the experimental results demonstrate higher recognition accuracy and robustness of our algorithm.