Improving vehicle localization using pole-like landmarks extracted from 3-D lidar scans

Self-driving vehicle systems have become mainstream as major international research and development companies compete for road detection and mapping projects such as localization. The latter plays an important role in self-driving systems because an accurate determination of the vehicle's position improves trajectory and offset results. And with a good localization function, a high-precision map can be established. This paper proposes using pole maps to enhance vehicle localization while correcting previous vehicle localization trajectory offsets. We have established a pole feature map with pole-shaped characteristic point clouds along the road that are marked with spatial coordinate information. We use three-dimensional light detection and ranging (3D Lidar) as a sensor for environmental scanning, dynamic segment thresholds, and point cloud projections to the image to extract poles, which avoids identification errors typically caused by the scattering effect of 3D Lidar. The resulting pole map uses the occupancy grid map to filter out noise. In terms of localization, we continue to detect pole features while the vehicle is moving, and compare these with the map. When the results match, we update the current localized information. The localization point corrects the information of the trajectory of the undetected pole, which improves vehicle localization accuracy.