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Active Localization Strategy for Hypotheses Pruning in Challenging Environments

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Abstract

Robust localization system has proven to be a cornerstone for mobile robot autonomy. Although passive robot localization is a mature field, it still could fail in challenging environments containing symmetries or open spaces. Active localization can fix this issue by allowing the robot to improve pose estimation by choosing specific actions. We propose an active localization strategy for the indoor position tracking problem in challenging environments. The proposed active localization is performed in three steps: (i) cluster the particle cloud with Spectral Clustering (or Kmeans\(++\)) algorithm, (ii) search and select the most informative point in a reduced search space, and (iii) execute rotational actions in order to sense the selected point. Hence, a significant number of wrong hypotheses are pruned. We also introduce a novel study that considers evaluates points in spatial neighborhoods all at once, instead of evaluating each cell independently. Simulated experiments show an improvement in robot pose estimation using the proposed strategy. Real-world validation in symmetric and open office-like environment is also presented.

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Data Availibility

The developed code and generated data for the Active Localization System presented in this research are available in [31]. If the reader has any doubts or needs, do not hesitate to contact the authors.

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Authors did not receive support from any organization for the submitted work.

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    Authors and Affiliations

    1. Instituto de Computación, Universidad de la República, 565 Julio Herrera y Reissig Ave., Montevideo, 11300, Montevideo, Uruguay

      Federico Andrade, Martín Llofriu, Mercedes Marzoa Tanco, Guillermo Trinidad Barnech & Gonzalo Tejera

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    1. Federico Andrade
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    2. Martín Llofriu
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    3. Mercedes Marzoa Tanco
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    4. Guillermo Trinidad Barnech
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    5. Gonzalo Tejera
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    Contributions

    All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Federico Andrade. The first draft of the manuscript was written by Federico Andrade. Federico Andrade and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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    Correspondence to Federico Andrade.

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    Martín Llofriu, Mercedes Marzoa Tanco, Guillermo Trinidad Barnech and Gonzalo Tejera are contributed equally to this work.

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    Andrade, F., Llofriu, M., Marzoa Tanco, M. et al. Active Localization Strategy for Hypotheses Pruning in Challenging Environments. J Intell Robot Syst 106, 47 (2022). https://doi.org/10.1007/s10846-022-01748-4

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