Road detection from dense lidar data based on local and global information

This paper proposes a novel algorithm of road detection from dense Light Detection and Ranging (LiDAR) data based on the local and global information of point clouds. First, the ground points and non-ground points are separated by a filtering algorithm. Then, road candidates are identified in the ground points by segmentation based on local intensity distribution histogram, which can make use of the homogeneity and consistency of roads. Finally, the ultimate road points are verified by global inference based on the area of the road candidate point sets, which can remove small data sets. The practical data in complex environment is utilized to test this algorithm. The experimental results show that this algorithm is capable of detecting roads automatically and efficiently, which has high robustness for complex roads and environments.