Autonomous Vehicle Localization on Standard Definition Maps Based on Camera and LiDAR Sensor Fusion

Localization is a crucial aspect of every autonomous driving vehicle, as determining its position in the navigation space enables the vehicle to safely plan its motion and interact with the environment. State-of-the-art approaches that rely on the Global Navigation Satellite System (GNSS) suffer from poor reliability in urban contexts. This issue can be overcome with Simultaneous Localization And Mapping (SLAM) methods. Generating and maintaining maps of large dimensions using these methods is a high-resource consuming task. This has motivated many to develop localization methods based on map databases provided by third-party sources. This paper presents a localization approach based on the OpenStreetMap (OSM) database. In particular, a local perception map generated from LiDAR and Camera observations is aligned to a graph representing an approximation of the road structure. Our method is validated through a comparison with a state-of-the-art approach.