Laser scanned point clouds to support autonomous vehicles

Autonomous vehicles are considered as the next major milestone in the history of transportation. Current vehicles are widely equipped with satellite-based positioning units and further sensors: cameras, laser scanner, and radar-based sensors. These new technologies are capable of not only detecting other vehicles, pedestrians or road obstacles, but of collecting information about the vehicles' neighborhood. If the vehicle positioning is to be extended by additional environmental information provided by these modern sensors, the available map database has to be prepared to be able to receive such data. Therefore map databases have to be extended to 3D and the map content must be improved. Such advanced 3D maps enable to receive, manage and integrate all data collected by the vehicles. These maps can support autonomous vehicle control, since such vehicles must continuously survey their close- and mid-range environment; not only other road users but also the partly changing road environment. The terrestrial and mobile laser scanners are excellent instruments to capture 3D data about roads and their environment. One big problem with the laser scanning is that it results huge point clouds with high geometric resolution, but - since it captures everything within its range - without any prior selection between more and less important details. Recording the measured points requires high storage capacity. The primary goal of the processing procedure is to extract the relevant content and transform it to useful and storage-optimized format. The paper discusses a workflow for basic laser scanned road data processing and demonstrates several use cases in storage and visualization.