Fuzzy ART-based place recognition for visual loop closure detection.

Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7502533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0770 (Electronic) Linking ISSN: 03401200 NLM ISO Abbreviation: Biol Cybern Subsets: MEDLINE

Publication: Berlin : Springer Verlag
Original Publication: Berlin, New York, Springer-Verlag.

Fuzzy Logic* ; Pattern Recognition, Automated*; Recognition, Psychology/*physiology; Algorithms ; Computer Simulation ; Humans ; Information Storage and Retrieval ; Mental Recall ; Photic Stimulation

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.

Erratum in: Biol Cybern. 2013 Apr;107(2):261.

Date Created: 20121211 Date Completed: 20130919 Latest Revision: 20191210

20250114

10.1007/s00422-012-0539-x

23224495