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MRS-VPR: a multi-resolution sampling based global visual place recognition method
- Publication Year :
- 2019
-
Abstract
- 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 achieving long-term localization under varying environmental conditions and changing viewpoints. It depends on a brute-force, time-consuming sequential matching method. We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, 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 has a much smaller scale than the reference sequence. Our experiments demonstrate that the proposed method is efficient in locating short temporary trajectories within long-term reference ones without losing accuracy compared to SeqSLAM.<br />Comment: 6 pages, 5 figures, ICRA 2019, accepted
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1902.10059
- Document Type :
- Working Paper