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A novel loop closure detection method in monocular SLAM

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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.

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Acknowledgments

This paper was supported by the Open Foundation of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education (No.2010A003), the Jiangsu Province Science Foundation (No.10KJB510014 and No. BK2012832) and the Natural Science Foundation of China (No.61104216 and No. 60805032).

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Authors and Affiliations

  1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China

    Liang Zhiwei, Gao Xiang, Chen Yanyan & Zhu Songhao

  2. Key Lab of MCCSE, Ministry of Education, Southeast University, Nanjing, 210096, China

    Liang Zhiwei

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  1. Liang Zhiwei
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  2. Gao Xiang
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  3. Chen Yanyan
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  4. Zhu Songhao
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Corresponding author

Correspondence to Liang Zhiwei.

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Zhiwei, L., Xiang, G., Yanyan, C. et al. A novel loop closure detection method in monocular SLAM. Intel Serv Robotics 6, 79–87 (2013). https://doi.org/10.1007/s11370-012-0124-0

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  • DOI: https://doi.org/10.1007/s11370-012-0124-0

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