| A multi-domain feature learning method for visual place recognition | |
Yin P(殷鹏)1; Xu LY(许凌云)1 ; Li, Xueqian3; Yin, Chen4; Li YL(李英立)1; Srivatsan, Rangaprasad Arun3; Li, Lu3; Ji JM(吉建民)2; He YQ(何玉庆)1
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| Department | 机器人学研究室 |
| Conference Name | 2019 International Conference on Robotics and Automation, ICRA 2019 |
| Conference Date | May 20-24, 2019 |
| Conference Place | Montreal, QC, Canada |
| Author of Source | Bosch ; DJI ; et al. ; Kinova ; Mercedes-Benz ; Samsung |
| Source Publication | 2019 International Conference on Robotics and Automation, ICRA 2019 |
| Publisher | IEEE |
| Publication Place | New York |
| 2019 | |
| Pages | 319-324 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20193507383961 |
| WOS ID | WOS:000494942300040 |
| Contribution Rank | 1 |
| ISSN | 1050-4729 |
| ISBN | 978-1-5386-6026-3 |
| Abstract | Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions. |
| Language | 英语 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/25515 |
| Collection | 机器人学研究室 |
| Corresponding Author | Yin P(殷鹏); Ji JM(吉建民) |
| Affiliation | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China 2.School of Computer Science and Technology, University of Science and Technology, Hefei, China 3.Biorobotics Lab, Robotics Institute, Carnegie Mellon University, Pittsburgh 4.PA 5.15213, United States 6.School of Computer Science, University of Beijing University of Posts and Telecommunications, Beijing, China |
| Recommended Citation GB/T 7714 | Yin P,Xu LY,Li, Xueqian,et al. A multi-domain feature learning method for visual place recognition[C]//Bosch, DJI, et al., Kinova, Mercedes-Benz, Samsung. New York:IEEE,2019:319-324. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| A multi-domain featu（289KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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