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Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS.

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Summary
This summary is machine-generated.

This study enhances autonomous vehicle localization by integrating Global Navigation Satellite System (GNSS) data into Monte Carlo Localization (MCL). This fusion improves accuracy in challenging environments like urban canyons, outperforming traditional methods.

Keywords:
GNSSGlobal Positioning System (GPS)LiDARautonomous drivinglocalizationmonte carloparticle filter

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Sensor Fusion

Background:

  • Accurate localization is critical for autonomous vehicle navigation.
  • Probabilistic laser localization methods like Monte Carlo Localization (MCL) can struggle in environments with sparse features or sensor noise.
  • Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) fusion is a common but not always optimal approach.

Purpose of the Study:

  • To develop an improved autonomous vehicle localization method.
  • To enhance particle weights in Monte Carlo Localization (MCL) by incorporating Global Navigation Satellite System (GNSS) data.
  • To address limitations of existing localization techniques in challenging environments.

Main Methods:

  • A modified Monte Carlo Localization (MCL) algorithm was developed.
  • Kalman filtered Global Navigation Satellite System (GNSS) information was integrated to enhance particle weights.
  • The proposed method was tested using the KITTI odometry dataset and an autonomous vehicle platform.

Main Results:

  • The proposed method demonstrated improved localization accuracy compared to classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL).
  • Enhanced particle weighting effectively improved localization in areas with fewer map features and prevented kidnapped robot scenarios.
  • The algorithm proved effective in difficult GNSS scenarios, such as urban canyons, by leveraging laser data.

Conclusions:

  • The integration of GNSS data into MCL significantly enhances autonomous vehicle localization accuracy and robustness.
  • The proposed method offers a superior solution for challenging navigation environments where traditional methods falter.
  • This approach provides a valuable advancement for the reliable deployment of autonomous vehicles.