Paper Info
Title
Fuzzy ART-based place recognition for visual loop closure detection
Abstract
The automatic place recognition problem is one of the key challenges in SLAM approaches for loop closure detection. Most of the appearance-based solutions to this problem share the idea of image feature extraction, memorization, and matching search. The weakness of these solutions is the storage and computational costs which increase drastically with the environment size. In this regard, the major constraints to overcome are the required visual information storage and the complexity of similarity computation. In this paper, a novel formulation is proposed that allows the computation time reduction while no visual information are stored and matched explicitly. The proposed solution relies on the incremental building of a bio-inspired visual memory using a Fuzzy ART network. This network considers the properties discovered in primate brain. The performance evaluation of the proposed method has been conducted using two datasets representing different large scale outdoor environments. The method has been compared with RatSLAM and FAB-MAP approaches and has demonstrated a decreased time and storage costs with broadly comparable precision recall performance.
Year
DOI
Venue
2013
10.1007/s00422-012-0539-x
Biological Cybernetics
Keywords
Field
DocType
Place recognition,Loop closure detection,View cells,Bio-inspired approach,Fuzzy ART,SLAM
Computer science,Precision and recall,Fuzzy logic,Visual memory,Information storage,Feature extraction,Artificial intelligence,Memorization,Machine learning,For loop,Computation
Journal
Volume
Issue
ISSN
107
2
1432-0770
Citations 
PageRank 
References 
2
0.38
28
Authors
3
Name
Order
Citations
PageRank
Karima Rebai1162.41
Ouahiba Azouaoui2367.12
Nouara Achour3143.39