Ground surface filtering of 3D point clouds based on hybrid regression technique

Lidar has received a lot of attention due to its precise ranging accuracy. Ground points filtering is an important task in point clouds processing. It's a challenge to model the ground surface and filter the point clouds accurately in the case of complex ground undulations, occlusions, and sparse point clouds. A novel ground surface modeling method based on a hybrid regression technique is proposed in this paper. The method integrates Gaussian process regression (GPR) and robust locally weighted regression (RLWR) by dividing the point clouds that are projected on the polar grid map into radial and circumferential filtering processes to form a hybrid regression model, which has the ability to eliminate the influence of outliers and model the ground surface robustly. First, the RLWR combined with gradient filter is applied to fit the sampled points in the radial direction, which will exclude outliers and get the fitting ground line. All radial fitting lines constitute the seed skeleton of the whole plane. Then, based on the seeds in the same circumferential of the skeleton, the GPR is applied to construct the ground surface model. The comparative experiments are implemented quantitatively and qualitatively on the simulated point clouds and measured data. The results show that the proposed method performs well in most real scenarios, even in the cases of ground undulation, occlusion, and sparse point clouds.