Publication: Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS
Loading...
Identifiers
Publication date
2020-06-02
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Impact
Export
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.
Description
Keywords
Localization, LiDAR, GNSS, Global Positioning System (GPS), Monte carlo, Particle filter, Autonomous driving
Bibliographic citation
de Miguel, M. Á., García, F., and Armingol, J. M. (2020). Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS. Sensors, 20(11), 3145