Robust Visual Place Recognition in Changing Environments Using Improved DTW
文献类型：期刊论文
| 作者 | Lu, Feng1,2; Chen, Baifan3; Guo, Zhaohong2; Zhou, Xiangdong2
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| 刊名 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 30期号:2页码:25 |
| ISSN号 | 0218-2130 |
| 关键词 | Place recognition loop closure detection mobile robots SLAM |
| 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 |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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