  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.
