Loop Closure Detection With Bidirectional Manifold Representation Consensus

Loop closure detection (LCD) is an indispensable module in simultaneous localization and mapping. It is responsible to recognize pre-visited areas during the navigation of a robot, providing auxiliary information to revise pose estimation. Unlike most current methods which focus on seeking an approp...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-04, Vol.24 (4), p.3949-3962
Main Authors: Zhang, Kaining, Li, Zizhuo, Ma, Jiayi
Format: Article
Language:English
Subjects:
Feature extraction
feature matching
Liquid crystal displays
Loop closure detection
Manifolds
Manifolds (mathematics)
Matching
Navigation
Optimization models
place recognition
Pose estimation
Representations
Simultaneous localization and mapping
SLAM
Task analysis
vision-based navigation
Visualization
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Description
Summary:Loop closure detection (LCD) is an indispensable module in simultaneous localization and mapping. It is responsible to recognize pre-visited areas during the navigation of a robot, providing auxiliary information to revise pose estimation. Unlike most current methods which focus on seeking an appropriate representation of images, we propose a novel two-stage pipeline dominated by the estimation of spatial geometric relationship. Specifically, to avoid unnecessary memory costs, consecutive images are segmented into sequences as per the similarity of their global features. Then the sequence descriptor is incrementally inserted into hierarchical navigable small world for the construction of reference database, from which the most similar image for the query one is searched parallelly. To further identify whether the candidate pair is geometry-consistent, a feature matching method termed as bidirectional manifold representation consensus (BMRC) is proposed. It constructs local neighborhood structures of feature points via manifold representation, and formulates the matching problem into an optimization model, enabling linearithmic time complexity via a closed-form solution. Meanwhile, an accelerated version of it is introduced (BMRC*), which performs about 63% faster than BMRC in an image pair with 352 initial correspondences. Extensive experiments on nine publicly available datasets demonstrate that BMRC and BMRC* perform well in feature matching and the proposed pipeline has remarkable performance in the LCD task.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3229364