Map-Matching-Based Localization Using Camera and Low-Cost GPS For Lane-Level Accuracy

For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a necessity 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 LiDAR price is still one of the big issues preventing this kind of solution from becoming wide-spread commodities. 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.