A novel loop closure detection method in monocular SLAM

Authors:
Liang Zhiwei;Gao Xiang;Chen Yanyan;Zhu Songhao
Affiliations:
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China 210046 and Key Lab of MCCSE, Ministry of Education, Southeast University, Nanjing, China 210096;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China 210046;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China 210046;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China 210046
Venue:
Intelligent Service Robotics
Year:
2013

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Abstract

This paper proposes an image appearance-based method to deal with the loop closure detection problem of monocular simultaneous localization and mapping for mobile robots. A bag-of-visual words approach is presented for building an appearance-based scene model. Subsequently, a fuzzy $$K$$ -means method is proposed to build a visual vocabulary synchronously. Each image can be represented by a vector of weighted words. The similarity between images is evaluated by the scalar product between the weighted vectors. A Bayesian filter algorithm is applied to update the detection probability and an inverse image retrieval method is employed to eliminate the wrong loop closure results. The experimental results demonstrate the efficiency of our proposed method.