Statistical terrain model with geometric feature detection based on GPU using LiDAR on vehicles

Abstract

Digital terrain models based on light detection and ranging (LiDAR) are widely used in ground robots to achieve autonomous navigation. Very few studies have been conducted on terrain modelling for preview information, which acts as an input for a vehicle’s active suspension system. In this study, we proposed an algorithm to construct a real-time terrain model from multi-frame LiDAR points according to the applications for preview control. The terrain model consisted of an elevation map with confidence intervals and feature detection. First, the statistical confidence interval of the elevation map represented the reliability of the mean height. Further, we used spatial signal processing techniques to detect the uneven geometric features of the terrain. The key point feature on the trajectory was detected by the Laplacian of Gaussian method. Finally, the raw point cloud data processing procedure was based on parallel multi-thread techniques combined with GPU programming, which significantly promotes computation efficiency to enable real-time implementation. Thus, the proposed terrain model integrates reliability issues and geometric features, which act as inputs for preview controllers or motion planning. Experimental results proved that our method has a competitive performance (in terms of precision and efficiency) compared with the state-of-the-art methods, and runs in real-time at 50 Hz.