Robust Visual Place Recognition Based on Context Information

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.