MRS-VPR: A multi-resolution sampling based global visual place recognition method
文献类型：会议论文
| 作者 | Li, Xueqian3; Xu LY(许凌云)5 ; Zhang HD(张宏达)5; He YQ(何玉庆)5; Ji JM(吉建民)4; Jia, Zhenzhong3; Li, Lu3; Chen, Yin2; Srivatsan, Rangaprasad Arun3; Yin P(殷鹏)5
|
| 出版日期 | 2019 |
| 会议日期 | May 20-24, 2019 |
| 会议地点 | Montreal, QC, Canada |
| 页码 | 7137-7142 |
| 英文摘要 | Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM. |
| 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
 |
| 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/173321/25518]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Yin P(殷鹏) |
| 作者单位 | 1.School of Computer Science, University of Beijing University of Posts and Telecommunications, Beijing, China 2.15213, United States 3.Biorobotics Lab, Robotics Institute, Carnegie Mellon University, Pittsburgh PA 4.School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui, China 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shenyang, Beijing, China |
| 推荐引用方式 GB/T 7714 | Li, Xueqian,Xu LY,Zhang HD,et al. MRS-VPR: A multi-resolution sampling based global visual place recognition method[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
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。