  Autonomous navigation of ground robots has been widely used in indoor
structured 2D environments, but there are still many challenges in outdoor 3D
unstructured environments, especially in rough, uneven terrains. This paper
proposed a plane-fitting based uneven terrain navigation framework (PUTN) to
solve this problem. The implementation of PUTN is divided into three steps.
First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based
algorithm called Plane Fitting RRT* (PF-RRT*) is proposed to obtain a sparse
trajectory. Each sampling point corresponds to a custom traversability index
and a fitted plane on the point cloud. These planes are connected in series to
form a traversable strip. Second, Gaussian Process Regression is used to
generate traversability of the dense trajectory interpolated from the sparse
trajectory, and the sampling tree is used as the training set. Finally, local
planning is performed using nonlinear model predictive control (NMPC). By
adding the traversability index and uncertainty to the cost function, and
adding obstacles generated by the real-time point cloud to the constraint
function, a safe motion planning algorithm with smooth speed and strong
robustness is available. Experiments in real scenarios are conducted to verify
the effectiveness of the method. The source code is released for the reference
of the community.
