  This paper presents a novel approach for local 3D environment representation
for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point
Clouds Mesh(OVPC Mesh). Our approach represents the surrounding of the robot as
a watertight 3D mesh generated from local point cloud data in order to
represent the free space surrounding the robot. It is a conservative estimation
of the free space and provides a desirable trade-off between representation
precision and computational efficiency, without having to discretize the
environment into a fixed grid size. Our experiments analyze the usability of
the approach for UGV navigation in rough terrain, both in simulation and in a
fully integrated real-world system. Additionally, we compare our approach to
well-known state-of-the-art solutions, such as Octomap and Elevation Mapping
and show that OVPC Mesh can provide reliable 3D information for trajectory
planning while fulfilling real-time constraints.
