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CSpace
Robust Visual Place Recognition in Changing Environments Using Improved DTW
Lu, Feng1,2; Chen, Baifan3; Guo, Zhaohong1; Zhou, Xiangdong1
2021-03-01
摘要Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n(3)) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM.
关键词Place recognition loop closure detection mobile robots SLAM
DOI10.1142/S0218213021500044
发表期刊INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
ISSN0218-2130
卷号30期号:2页码:25
通讯作者Chen, Baifan(chenbaifan@csu.edu.cn)
收录类别SCI
WOS记录号WOS:000634941800004
语种英语