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Neighborhood Manifold Preserving Matching for Visual Place Recognition | IEEE Journals & Magazine | IEEE Xplore

Neighborhood Manifold Preserving Matching for Visual Place Recognition


Abstract:

This article proposes an effective and efficient visual place recognition (VPR) approach, which can make full use of semantic, sequential, and spatial geometric informati...Show More

Abstract:

This article proposes an effective and efficient visual place recognition (VPR) approach, which can make full use of semantic, sequential, and spatial geometric information in VPR tasks. Rather than previous methods focusing on extracting discriminative and compact features to represent images, we improve VPR performance from candidate selection and geometric verification. To this end, we propose a fast feature matching algorithm for real-time geometrical verification of candidate places, termed neighborhood manifold preserving matching (NMP). To generate high-quality candidates, we design a dynamic sequence partitioning strategy based on NMP, which is able to utilize the inherently sequential nature of spatial data to cluster images into places. By sequence-to-sequence matching, the ambiguity of single frame matching can be reduced. Extensive experiments demonstrate that our VPR method outperforms the current state-of-the-art methods, and our geometric verification and candidate selection strategies are easily plugged into other VPR pipelines to significantly improve the VPR performance.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 19, Issue: 7, July 2023)
Page(s): 8127 - 8136
Date of Publication: 25 October 2022

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