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An Enhanced Adaptive Monte Carlo Localization for Service Robots in Dynamic and Featureless Environments

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Abstract

The Adaptive Monte Carlo Localization (AMCL) is a common technique for mobile robot localization problem. However, AMCL performs poorly on localization when robot navigates to a featureless environment. To address this issue, an enhanced AMCL is proposed through using the information from laser scan points to improve the preciseness and robustness of the localization problem for service robots. The proposed new method first matches the laser scan points with a pre-built grid map by an iterative closest point (ICP) algorithm and then designs a Localization Confidence Estimation (LCE) method to evaluate the localization credibility of ICP and AMCL respectively. Finally, the ICPs with high LCE scores are selected to inject particle swarms in the form of particles with adaptive amounts to optimize the next step of the AMCL estimation process. With the improved method, AMCL’s particle swarm can quickly converge to the correct position after several iterations. Experimental results show that the proposed algorithm outperforms the original AMCL in respect of accuracy and robustness even in dynamic environments.

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Funding

This work was partially supported by the National Science Foundation of China (52172376), the Young Scientists Fund of the National Natural Science Foundation of China (52002013), and the China Postdoctoral Science Foundation (BX20200036, 2020M680298).

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Authors and Affiliations

  1. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Shan He, Tao Song, Pengcheng Wang, Chuan Ding & Xinkai Wu

  2. Beijing Robint Technology Co. Ltd., Beijing, 100191, China

    Shan He & Tao Song

  3. Zhongguancun Laboratory, Beijing, 100191, China

    Xinkai Wu

Authors
  1. Shan He
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  2. Tao Song
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  3. Pengcheng Wang
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  4. Chuan Ding
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  5. Xinkai Wu
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Contributions

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

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Correspondence to Xinkai Wu.

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He, S., Song, T., Wang, P. et al. An Enhanced Adaptive Monte Carlo Localization for Service Robots in Dynamic and Featureless Environments. J Intell Robot Syst 108, 6 (2023). https://doi.org/10.1007/s10846-023-01858-7

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