Abstract：Aiming at the problem of loop closure detection in monocular simultaneous localization and mapping for mobile robots,a detection algorithm based on visual dictionary (VD) is presented. Firstly, feature extraction is performed for each required image using SURF methods. Subsequently,a fuzzy K-means algorithm is employed to cluster these visual feature vectors into visual words based on VD which is constructed online. To precisely represent the similarities between each visual word and corresponding local visual features ,Gaussian mixture model is proposed to learn the probability model of every visual word in bags of visual words. Consequently,every image can be denoted as a probabilistic vector of VD,and thus the similarities between any two images can be computed based on vector inner product. To guarantee the continuity of the closed-loop detection,a Bayesian filter method is applied to fuse historical closed-loop detection information and the obtained similarities to calculate the posterior probability distribution of closed-loop hypothesis. Furthermore,two memory management mechanisms,shallow memory and deep memory,are introduced to improve the process speed of the proposed algorithm. The experimental results demonstrate the validity of the proposed approach.
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2013, Vol. 26 
