Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy
Rahmad Sadli,
Mohamed Afkir,
Abdenour Hadid,
Atika Rivenq,
Abdelmalik Taleb-Ahmed
Affiliations
Rahmad Sadli
Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, University of Lille, CNRS, Centrale Lille, F-59313 Valenciennes, France
Mohamed Afkir
Transalley Technopole, 59300 Famars, France
Abdenour Hadid
Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, University of Lille, CNRS, Centrale Lille, F-59313 Valenciennes, France
Atika Rivenq
Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, University of Lille, CNRS, Centrale Lille, F-59313 Valenciennes, France
Abdelmalik Taleb-Ahmed
Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, University of Lille, CNRS, Centrale Lille, F-59313 Valenciennes, France
For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV’s maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.
