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MRS-VPR: A multi-resolution sampling based global visual place recognition method
Yin P(殷鹏)1; Srivatsan, Rangaprasad Arun3; Chen, Yin4; Li, Xueqian3; Zhang HD(张宏达)1; Xu LY(许凌云)1; Li, Lu3; Jia, Zhenzhong3; Ji JM(吉建民)2; He YQ(何玉庆)1
Department机器人学研究室
Conference Name2019 International Conference on Robotics and Automation, ICRA 2019
Conference DateMay 20-24, 2019
Conference PlaceMontreal, QC, Canada
Author of SourceBosch ; DJI ; et al. ; Kinova ; Mercedes-Benz ; Samsung
Source Publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages7137-7142
Indexed ByEI
EI Accession number20193507383712
Contribution Rank1
ISSN1050-4729
ISBN978-1-5386-6026-3
AbstractPlace 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25518
Collection机器人学研究室
Corresponding AuthorYin P(殷鹏)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shenyang, Beijing, China
2.School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui, China
3.Biorobotics Lab, Robotics Institute, Carnegie Mellon University, Pittsburgh PA
4.15213, United States
5.School of Computer Science, University of Beijing University of Posts and Telecommunications, Beijing, China
Recommended Citation
GB/T 7714
Yin P,Srivatsan, Rangaprasad Arun,Chen, Yin,et al. MRS-VPR: A multi-resolution sampling based global visual place recognition method[C]//Bosch, DJI, et al., Kinova, Mercedes-Benz, Samsung. New York:IEEE,2019:7137-7142.
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