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A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm.

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This study introduces a weighted parallel iterative closest point (WP-ICP) algorithm for indoor robot localization using only LiDAR. The method efficiently reduces computation and enhances accuracy by matching features like corners and lines.

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Indoor robot localization typically requires multiple sensors, increasing cost and computation.
  • Existing methods may struggle with environmental uncertainties and computational load.

Purpose of the Study:

  • To propose an efficient and robust indoor localization algorithm using a single Light Detection and Ranging (LiDAR) sensor.
  • To reduce computational effort while maintaining localization precision.

Main Methods:

  • A weighted parallel iterative closest point (WP-ICP) algorithm with interpolation is developed.
  • Point cloud processing extracts corner and line features for targeted registration.
  • Fusion of ICP confidence levels enhances robustness against environmental uncertainties.

Main Results:

  • The WP-ICP method significantly reduces computational requirements compared to traditional ICP.
  • Feature-based matching (corners and lines) minimizes mismatches and ICP iterations.
  • Experiments demonstrate effective localization precision in both clean and perturbed indoor environments.

Conclusions:

  • The proposed WP-ICP algorithm offers an efficient and precise solution for indoor robot localization using LiDAR.
  • This approach addresses the limitations of multi-sensor systems by reducing cost and computational load.
  • The feature-matching strategy and confidence fusion contribute to robust and accurate pose estimation.