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Single-Line LiDAR Localization via Contribution Sampling and Map Update Technology.

Xiaoxu Jiang1, David K Yang2, Zhenyu Tian1

  • 1Lab for High Technology, Tsinghua University, Beijing 100084, China.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for robust robot localization using single-line lidar. The Contribution Sampling and Map-Updating Localization (CSMUL) algorithm enhances accuracy in challenging environments with changing maps.

Keywords:
contribution samplingdegenerate environmentslocalizationmap updatingrobustnesssingle-line LiDAR

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Single-line lidar localization is crucial for robotics but faces challenges like map changes and sensor degradation.
  • Existing methods struggle with robustness in dynamic environments and during sensor signal loss.

Purpose of the Study:

  • To develop a robust localization algorithm for single-line lidar systems.
  • To enhance robot navigation in environments with frequent map alterations and sensor degeneracies.

Main Methods:

  • Introduced the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm.
  • Implemented weighted contribution sampling for degenerate environments.
  • Developed real-time dynamic map-updating for anomaly detection and correction.

Main Results:

  • CSMUL demonstrated significantly enhanced robustness in degenerate lidar conditions.
  • The algorithm achieved superior accuracy in dynamic map scenarios.
  • Real-world tests confirmed CSMUL's ability to handle map changes and ensure continuous positioning.

Conclusions:

  • CSMUL effectively overcomes limitations of traditional lidar localization.
  • The algorithm provides a reliable solution for real-time robot positioning in complex, changing environments.
  • This work advances the practical application of lidar-based localization in diverse robotic fields.