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Scan Matching-Based Particle Filter for LIDAR-Only Localization.

Nagavenkat Adurthi1

  • 1Mechanical and Aerospace Engineering, University of Alabama in Huntsville, 301 Sparkman Dr., Alabama, AL 35824, USA.

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Summary

This study introduces an efficient localization method for autonomous vehicles using 3D LIDAR data. The approach combines particle filters with scan matching, improving real-time performance for accurate vehicle pose estimation.

Keywords:
LIDARlocalizationparticle filteringscan matching

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Accurate vehicle localization is crucial for autonomous driving.
  • Existing particle filter methods for localization are computationally intensive.
  • Real-time performance is limited by the computational cost of LIDAR scan likelihood calculations.

Purpose of the Study:

  • To develop an efficient localization methodology for autonomous vehicles using only 3D LIDAR.
  • To overcome the computational limitations of traditional particle filters for real-time localization.
  • To enhance the resampling stage of particle filters through a hybrid global-local scan matching approach.

Main Methods:

  • Development of a hybrid localization approach combining particle filters and global-local scan matching.
  • Utilizing a pre-computed likelihood grid to accelerate LIDAR scan likelihood computations.
  • Scan matching-based particle filters adapted for vehicle pose and state estimation.

Main Results:

  • Demonstrated efficacy of the proposed hybrid approach using simulation data from KITTI datasets.
  • The method effectively informs the resampling stage of the particle filter.
  • Improved computational efficiency for real-time autonomous vehicle localization.

Conclusions:

  • The proposed hybrid localization method offers a computationally efficient solution for autonomous vehicles using 3D LIDAR.
  • The integration of global-local scan matching significantly enhances particle filter performance.
  • This methodology advances the state-of-the-art in real-time vehicle localization systems.