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Vision-Based Hybrid Map-Building and Robot Localization in Unstructured and Moderately Dynamic Environments

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Novel Applications of Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 586))

Abstract

This work focuses on developing efficient environment representation and localization for mobile robots. A solution-approach is proposed for hybrid map-building and localization, which suits operating environments with unstructuredness and moderate dynamics. The solution-approach is vision-based and includes two phases. In the first phase, the map-building reduces the domain of extracted point features from local places through an information-theoretic analysis. The analysis simultaneously selects the most distinctive features only. The selected features are further compressed into codewords. The uncompressed features are also tagged with their metric position. In such a way, a unified map is created with hybrid data representation. In the second phase, the map is used to localize the robot. For fast topological localization, features extracted from the local place are compared to the codewords. To extend the localization into a metric pose, triangulation is executed hierarchically for the identified topological place with the use of the positional metric data of features. To ensure accurate position estimate, the dynamics of the environment are detected through the spatial layout of features and are isolated at the metric localization level. The proposed map-building and localization solution enables for a fast hybrid localization without degenerating the accuracy of localization.

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Notes

  1. 1.

    The feature descriptor will be outlined as feature in the paper for easiness.

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Authors and Affiliations

  1. Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Abbassia, Egypt

    Sherine Rady

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  1. Sherine Rady
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Correspondence to Sherine Rady .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Mincho Hadjiski

  2. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  3. Research & Advanced Engineering, Ford Motor Company, Dearborn, Michigan, USA

    Dimitar Filev

  4. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Vladimir Jotsov

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Rady, S. (2016). Vision-Based Hybrid Map-Building and Robot Localization in Unstructured and Moderately Dynamic Environments. In: Hadjiski, M., Kasabov, N., Filev, D., Jotsov, V. (eds) Novel Applications of Intelligent Systems. Studies in Computational Intelligence, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-14194-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-14194-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14193-0

  • Online ISBN: 978-3-319-14194-7

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