Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS Articles uri icon

publication date

  • June 2020

start page

  • 1

end page

  • 13

issue

  • 11

volume

  • 20

International Standard Serial Number (ISSN)

  • 1424-8220

abstract

  • This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

keywords

  • localization; lidar; gnss; global positioning system (gps); monte carlo; particle filter; autonomous driving