  A Gaussian Process GP based ground segmentation method is proposed in this
paper which is fully developed in a probabilistic framework. The proposed
method tends to obtain a continuous realistic model of the ground. The LiDAR
three-dimensional point cloud data is used as the sole source of the input
data. The physical realities of the data are taken into account to properly
classify sloped ground as well as the flat ones. Furthermore, unlike
conventional ground segmentation methods, no height or distance constraints or
limitations are required for the algorithm to be applied to take all the
regarding physical behavior of the ground into account. Furthermore, a
density-like parameter is defined to handle ground-like obstacle points in the
ground candidate set. The non-stationary covariance kernel function is used for
the Gaussian Process, by which Bayesian inference is applied using the maximum
A Posteriori criterion. The log-marginal likelihood function is assumed to be a
multi-task objective function, to represent a whole-frame unbiased view of the
ground at each frame. Simulation results show the effectiveness of the proposed
method even in an uneven, rough scene which outperforms similar Gaussian
process-based ground segmentation methods.
