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A Real-Time Global Re-Localization Framework for a 3D LiDAR-Based Navigation System.

Ziqi Chai1, Chao Liu1,2, Zhenhua Xiong1

  • 1State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

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|October 16, 2024
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Summary
This summary is machine-generated.

This study introduces a novel template-matching framework for robot global re-localization, enabling navigation in previously unvisited environments using heterogeneous sensor maps. The system achieves high accuracy and efficiency for autonomous navigation.

Keywords:
LiDAR SLAMglobal re-localizationplace recognitionreal-time performancetemplate matching

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Area of Science:

  • Robotics
  • Computer Vision
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Place recognition is crucial for robot navigation and re-localization in pre-built maps.
  • Current methods are limited to previously visited places and require identical sensors, often being time-consuming.

Purpose of the Study:

  • To propose a template-matching-based global re-localization framework overcoming limitations of existing methods.
  • To enable robots to re-localize in environments not previously visited and with heterogeneous sensor data.

Main Methods:

  • An offline stage involves resampling virtual LiDAR scans, extracting rotation-invariant descriptors as templates, and clustering them into a library.
  • An online stage employs a cascade coarse-to-fine template matching for efficient and accurate re-localization.
  • The framework supports maps built by the robot or other heterogeneous sensors.

Main Results:

  • Achieved 99% success rate at 11 Hz in simulations with 100K templates (1.0m error threshold).
  • Validated with 94.67% success rate at 7 Hz on The Newer College Dataset with 40K templates (1.0m error threshold).

Conclusions:

  • The proposed framework demonstrates high accuracy and efficiency for global re-localization.
  • It successfully enables re-localization in heterogeneous maps and previously unvisited environments.