MRS-VPR: a multi-resolution sampling based global visual place recognition method
Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen, Xueqian Li, Hongda Zhang, Lingyun Xu, Lu Li, Zhenzhong Jia
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 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.
BibTeX
@inproceedings{icra2019_mrsvpramultireso,
title = {MRS-VPR: a multi-resolution sampling based global visual place recognition method},
author = {Peng Yin and Rangaprasad Arun Srivatsan and Yin Chen and Xueqian Li and Hongda Zhang and Lingyun Xu and Lu Li and Zhenzhong Jia and Jianmin Ji and Yuqing He},
booktitle = {ICRA 2019},
year = {2019}
}