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Updated: Jul 31, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Localization on a-priori information of plane extraction.

Junjie Ji1, Jing-Shan Zhao1, Sergey Yurievich Misyurin2,3

  • 1Department of Mechanical Engineering, Tsinghua University, Beijing, China.

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Summary

This study introduces a new indoor localization method for autonomous mobile robots using prior plane information. The technique enhances robot positioning accuracy by leveraging multi-plane constraints and real-time point cloud data.

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Area of Science:

  • Robotics
  • Computer Vision
  • Geospatial Analysis

Background:

  • Indoor localization is crucial for autonomous mobile robots.
  • Flattened walls are common references for robot navigation.
  • Existing methods may not fully utilize prior environmental data.

Purpose of the Study:

  • To develop a novel localization technique for autonomous mobile robots.
  • To utilize a-priori plane point cloud data for enhanced positioning.
  • To enable robust localization using real-time multi-plane constraints.

Main Methods:

  • A-priori plane point cloud extraction from known environments (e.g., BIM).
  • Real-time multi-plane constraint estimation for robot pose.
  • An extended image coordinate system for plane representation and correspondence.
  • Region of Interest (ROI) filtering for relevant point cloud data.

Main Results:

  • Successful estimation of robot position and pose using multi-plane constraints.
  • Demonstrated robustness to initial position and pose errors.
  • Validation of the method's effectiveness through experimental testing.

Conclusions:

  • The proposed method offers a reliable approach for indoor robot localization.
  • Leveraging prior plane information significantly improves localization accuracy.
  • The technique provides redundancy and is tolerant to initial localization uncertainties.