Abstract
The integration of Light Detection and Ranging (LiDAR) and Global Navigation Satellite System (GNSS) technologies marks a significant advancement in the fields of autonomous driving and intelligent transportation systems. This research introduces a methodology for geolocalizing road objects, specifically poles, by leveraging the detailed spatial data from LiDAR combined with the location capabilities of GNSS, while carefully accounting for these sensor offsets. Our approach takes advantage of the synergy between LiDAR’s exceptional spatial resolution and GNSS’s global positioning capability. This precision is crucial for the navigation systems of autonomous vehicles. By processing LiDAR data to detect objects and calculate their positions relative to the sensor, and then transforming these positions into global coordinates using inverse geodesic calculations, we present a methodology that can perform object geolocation in various environments. This paper details the development of the methodology, the challenges encountered, and the solutions devised, showcasing the approach’s performance through experimental results and suggests future directions for further research.