Mobile 3D Vision: LiDAR Assisted Car Navigation and Mapping using Deep Learning on Point Clouds

In Mobile Laser Scanning (MLS), LiDAR scanners are mounted on cars to scan along roads with various applications such as road inspections, mapping or navigation. The accuracy of point clouds is a key requirement of such measurement campaigns, of which a prerequisite is the careful modeling of the trajectory and its accurate estimation. This is challenging to obtain in practice because loss of GNSS signal or degradation of its quality are common events when scanning from the road.

In this project, you will contribute on improving a real scanning system operated by a Swiss company, orbis360. The project will focus on adapting a deep learning methodology developed in our lab and able to recognize correspondences (i.e. recognizable points scanned multiple times in the point cloud). The end goal is to improve the robustness of the trajectory estimation and point cloud generation pipeline when GNSS signal degradation occurs, allowing for more accurate 3D digitization of scanned areas.