  Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in
unknown environments. A novel $\mathcal{GP}$-based method is proposed for the
ground segmentation task in rough driving scenarios. A non-stationary
covariance function is utilized as the kernel for the $\mathcal{GP}$. The
ground surface behavior is assumed to only demonstrate local-smoothness. Thus,
point estimates of the kernel's length-scales are obtained. Thus, two Gaussian
processes are introduced to separately model the observation and local
characteristics of the data. While, the \textit{observation process} is used to
model the ground, the \textit{latent process} is put on length-scale values to
estimate point values of length-scales at each input location. Input locations
for this latent process are chosen in a physically-motivated procedure to
represent an intuition about ground condition. Furthermore, an intuitive guess
of length-scale value is represented by assuming the existence of hypothetical
surfaces in the environment that every bunch of data points may be assumed to
be resulted from measurements from this surfaces. Bayesian inference is
implemented using \textit{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 shows the effectiveness of the proposed method even in an uneven, rough
scene which outperforms similar Gaussian process based ground segmentation
methods. While adjacent segments do not have similar ground structure in an
uneven scene, the proposed method gives an efficient ground estimation based on
a whole-frame viewpoint instead of just estimating segment-wise probable ground
surfaces.
