Robust Visual Place Recognition in Changing Environments Using Improved DTW

doi:10.1142/S0218213021500044

	Robust Visual Place Recognition in Changing Environments Using Improved DTW
	Lu, Feng 1,2; Chen, Baifan 3; Guo, Zhaohong 2; Zhou, Xiangdong 2
刊名	INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
	2021-03-01
卷号	30 期号:2 页码:25
关键词	Place recognition loop closure detection mobile robots SLAM
ISSN号	0218-2130
DOI	10.1142/S0218213021500044
通讯作者	Chen, Baifan(chenbaifan@csu.edu.cn)
英文摘要	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.
资助项目	National Key Research and Development Plan[2018YFB1201602] ; National Natural Science Foundation of China[61802361] ; Technical Innovation and Application Development Project of Chongqing Province[cstc2019jscx-msxmX0424]
WOS研究方向	Computer Science
语种	英语
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
WOS记录号	WOS:000634941800004
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/13246]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Chen, Baifan
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China 3.Cent South Univ, Sch Automat, Changsha, Peoples R China
推荐引用方式 GB/T 7714	Lu, Feng,Chen, Baifan,Guo, Zhaohong,et al. Robust Visual Place Recognition in Changing Environments Using Improved DTW[J]. INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS,2021,30(2):25.
APA	Lu, Feng,Chen, Baifan,Guo, Zhaohong,&Zhou, Xiangdong.(2021).Robust Visual Place Recognition in Changing Environments Using Improved DTW.INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS,30(2),25.
MLA	Lu, Feng,et al."Robust Visual Place Recognition in Changing Environments Using Improved DTW".INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 30.2(2021):25.