Paper Info
Title
Loop Closure Detection With Reweighting NetVLAD and Local Motion and Structure Consensus
Abstract
Dear Editor, Loop closure detection (LCD) is an important module in simultaneous localization and mapping (SLAM). In this letter, we address the LCD task from the semantic aspect to the geometric one. To this end, a network termed as AttentionNetVLAD which can simultaneously extract global and local features is proposed. It leverages attentive selection for local features, coupling with reweighting the soft assignment in NetVLAD via the attention map for global features. Given a query image, candidate frames are first identified coarsely by retrieving similar global features in the database via hierarchical navigable small world (HNSW). As global features mainly summarize the semantic information of images and lead to compact representation, information about spatial arrangement of visual elements is lost. To provide fine results, we further propose a feature matching method termed as local motion and structure consensus (LMSC) to conduct geometric verification between candidate pairs. It constructs local neighborhood structures of local features through motion consistency and manifold representation, and formulates the matching problem into an optimization model, enabling linearithmic time complexity via a closed-form solution. Experiments on several public datasets demonstrate that LMSC performs well in feature matching, and the proposed LCD system can yield satisfying results.
Year
DOI
Venue
2022
10.1109/JAS.2022.105635
IEEE/CAA Journal of Automatica Sinica
Keywords
DocType
Volume
structure consensus,Loop closure detection,LCD task,local features,NetVLAD,attention map,similar global features,feature matching method,local motion,local neighborhood structures,motion consistency,manifold representation
Journal
9
Issue
ISSN
Citations 
6
2329-9266
0
PageRank 
References 
Authors
0.34
21
3
Name
Order
Citations
PageRank
Kaining Zhang122.06
Jiayi Ma2130265.86
Junjun Jiang3113874.49