STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition
文献类型：期刊论文
| 作者 | Lu, Feng2,4; Chen, Baifan1; Zhou, Xiang-Dong2,4 ; Song, Dezhen3
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| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
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| 出版日期 | 2021-07-01 |
| 卷号 | 6期号:3页码:4297-4304 |
| 关键词 | Deep learning for visual perception localization vision-based navigation |
| ISSN号 | 2377-3766 |
| DOI | 10.1109/LRA.2021.3067623 |
| 通讯作者 | Chen, Baifan(chenbaifan@csu.edu.cn) |
| 英文摘要 | Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the middle layers of CNNs are more robust to drastic appearance changes than handcrafted features and high-layer features. Unfortunately, the holistic mid-layer features lack robustness to large viewpoint changes. Here we split the holistic mid-layer features into local features, and propose an adaptive dynamic time warping (DTW) algorithm to align local features from the spatial domain while measuring the distance between two images. This realizes viewpoint-invariant and condition-invariant place recognition. Meanwhile, a local matching DTW (LM-DTW) algorithm is applied to perform image sequence matching based on temporal alignment, which achieves further improvements and ensures linear time complexity. We perform extensive experiments on five representative VPR datasets. The results show that the proposed method significantly improves the CNN-based methods. Moreover, our method outperforms several state-of-the-art methods while maintaining good run-time performance. This work provides a novel way to boost the performance of CNN methods without any re-training for VPR. The code is available at https://github.com/Lu-Feng/STA-VPR. |
| 资助项目 | National Key R&D Program of China[2018YFB1201602] ; National Natural Science Foundation of China[61976224] ; National Natural Science Foundation of China[61802361] |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| WOS记录号 | WOS:000639767800007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13390]  |
| 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.Cent South Univ, Sch Automat, Changsha 410083, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Texas A&M Univ, CSE Dept, College Stn, TX 77843 USA 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Feng,Chen, Baifan,Zhou, Xiang-Dong,et al. STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2021,6(3):4297-4304. |
| APA | Lu, Feng,Chen, Baifan,Zhou, Xiang-Dong,&Song, Dezhen.(2021).STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition.IEEE ROBOTICS AND AUTOMATION LETTERS,6(3),4297-4304. |
| MLA | Lu, Feng,et al."STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition".IEEE ROBOTICS AND AUTOMATION LETTERS 6.3(2021):4297-4304. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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