Open AccessCNN Feature Boosted SeqSLAM for Real‐Time Loop Closure Detection
Author(s) -
BAI Dongdong,
WANG Chaoqun,
ZHANG Bo,
YI Xiaodong,
YANG Xuejun
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.03.010
Subject(s) - computer science , convolutional neural network , feature (linguistics) , artificial intelligence , matching (statistics) , closure (psychology) , pattern recognition (psychology) , layer (electronics) , image (mathematics) , range (aeronautics) , algorithm , mathematics , philosophy , statistics , linguistics , chemistry , materials science , organic chemistry , economics , market economy , composite material
This paper proposes an efficient and robust Loop closure detection (LCD) method based on Convolutional neural network (CNN) feature. The primary method is called SeqCNNSLAM, in which both the outputs of the intermediate layer of a pre‐trained CNN and the outputs of traditional sequence‐based matching procedure are incorporated, making it possible to handle the viewpoint and condition variance properly. An acceleration algorithm for SeqCNNSLAM is developed to reduce the search range for the current image, resulting in a new LCD method called A‐SeqCNNSLAM. To improve the applicability of A‐SeqCNNSLAM to new environments, O‐SeqCNNSLAM is proposed for online parameters adjustment in A‐SeqCNNSLAM. In addition to the above work, we further put forward a promising idea to enhance SeqSLAM by integrating the both CNN features and VLAD's advantages called patch based SeqCNNSLAM (P‐SeqCNNSLAM), and provide some preliminary experimental results to reveal its performance.