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A Robust Feature Matching Method for Robot Localization in a Dynamic Indoor Environment

  • Conference paper
Technologies and Applications of Artificial Intelligence (TAAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8916))

Abstract

In this paper, we report how the feature matching method can be applied to deal with the indoor mobile robot localization problem. We assume that a robot equipped with a laser rangefinder can scan the environment in real time and get the geometry features, and then the robot can match these features with those collected in advance to find the possible locations. This approach would face two difficulties. Since there are locations with similar features, the robot have to move around and do the scan and match several times to make sure the right location. There is another difficult problem, the features might not be fix in real-world dynamic environment, e.g. people might be walking through, furniture might be shifted; therefore, a robust feature matching method is needed for dynamic environment. This paper describes an efficient method using omni-directional feature grouping to improve the feature matching method for robot localization. With the laser rangefinder, a robot finds the 360 degree coverage information. Omni-directional feature grouping has the advantage of dividing all the features of a hypothetical position through different directions to generate multiple sets of environmental features. The method can reduce the affection of moving objects in a dynamic environment. Experimental results show that our method improve the accuracy rate and has low average errors.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City, 41349, Taiwan (R.O.C)

    Tsung-Yen Tsou & Shih-Hung Wu

Authors
  1. Tsung-Yen Tsou
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  2. Shih-Hung Wu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Da’an Dist., 106, Taipei City, Taiwan

    Shin-Ming Cheng

  2. Department of Information Management, Tamkang University, No. 151, Yingzhuan Rd., Danshui Dist., 25137, New Taipei City, Taiwan

    Min-Yuh Day

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Tsou, TY., Wu, SH. (2014). A Robust Feature Matching Method for Robot Localization in a Dynamic Indoor Environment. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_33

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  • DOI: https://doi.org/10.1007/978-3-319-13987-6_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13986-9

  • Online ISBN: 978-3-319-13987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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