A multi-domain feature learning method for visual place recognition
文献类型：会议论文
| 作者 | Li, Xueqian4; Yin, Chen1; Li YL(李英立)6; Srivatsan, Rangaprasad Arun4; Li, Lu4; He YQ(何玉庆)6 ; Ji JM(吉建民)5; Yin P(殷鹏)6; Xu LY(许凌云)6
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| 出版日期 | 2019 |
| 会议日期 | May 20-24, 2019 |
| 会议地点 | Montreal, QC, Canada |
| 页码 | 319-324 |
| 英文摘要 | 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. |
| 源文献作者 | Bosch ; DJI ; et al. ; Kinova ; Mercedes-Benz ; Samsung |
| 产权排序 | 1 |
| 会议录 | 2019 International Conference on Robotics and Automation, ICRA 2019
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 1050-4729 |
| ISBN号 | 978-1-5386-6026-3 |
| 源URL | [http://ir.sia.cn/handle/173321/25515]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Ji JM(吉建民); Yin P(殷鹏) |
| 作者单位 | 1.PA 2.School of Computer Science, University of Beijing University of Posts and Telecommunications, Beijing, China 3.15213, United States 4.Biorobotics Lab, Robotics Institute, Carnegie Mellon University, Pittsburgh 5.School of Computer Science and Technology, University of Science and Technology, Hefei, China 6.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Li, Xueqian,Yin, Chen,Li YL,et al. A multi-domain feature learning method for visual place recognition[C]. 见:. Montreal, QC, Canada. May 20-24, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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