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Reliable and Fast Localization in Ambiguous Environments Using Ambiguity Grid Map.

Gen Li1, Jie Meng1, Yuanlong Xie1

  • 1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

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Summary
This summary is machine-generated.

This study introduces a new robotic localization method using an ambiguity grid map (AGM) to handle challenging environments. The AGM-based adaptive Monte Carlo localization improves accuracy and efficiency in areas with symmetry or few features.

Keywords:
Dynamic Bayes networkMonte Carlo localizationambiguous environmentnavigationperceptual aliasing

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Robotic navigation in real-world environments faces challenges due to perceptual aliasing in symmetrical or featureless areas.
  • Accumulated localization errors in such ambiguous environments hinder accurate robot positioning.

Purpose of the Study:

  • To propose a novel probabilistic localization method to address accumulated localization errors in ambiguous environments.
  • To introduce the ambiguity grid map (AGM) for evaluating environmental ambiguity and estimating localization errors.

Main Methods:

  • Developed an AGM-based adaptive Monte Carlo localization method utilizing an improved Dynamic Bayes network.
  • Integrated a portal motion model for reliable pose prediction and immediate correction of accumulated localization errors.
  • The AGM quantifies environmental ambiguity and estimates potential localization errors.

Main Results:

  • The proposed method demonstrated improved localization reliability in simulations and real-world experiments.
  • The AGM-based approach effectively handles perceptual aliasing caused by environmental symmetries or featureless regions.
  • The portal motion model ensures efficient and accurate pose prediction.

Conclusions:

  • The AGM-based adaptive Monte Carlo localization method offers a robust solution for reliable robot navigation in ambiguous environments.
  • The integration of AGM and a portal motion model enhances localization accuracy and efficiency.
  • This approach mitigates the impact of accumulated localization errors, improving overall robotic system performance.