High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles

Abstract

In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.