MRS-VPR: a multi-resolution sampling based global visual place recognition method

Peng Yin, Arun Srivatsan Rangaprasad, Yin Chen, Xueqian Li, Hongda Zhang, Lingyun Xu, Lu Li, Zenzhong Jia, Jiamin Ji and Yuqin He
Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), May, 2019

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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 filterbased 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 MRS-VPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.


@conference{Yin-2019-112129,
author = {Peng Yin and Arun Srivatsan Rangaprasad and Yin Chen and Xueqian Li and Hongda Zhang and Lingyun Xu and Lu Li and Zenzhong Jia and Jiamin Ji and Yuqin He},
title = {MRS-VPR: a multi-resolution sampling based global visual place recognition method},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
year = {2019},
month = {May},
publisher = {IEEE},
} 2019-03-12T08:16:28-04:00