OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation
This addresses the need for efficient and precise free-space representation in autonomous ground vehicle navigation, particularly in rough terrain, though it appears incremental compared to existing methods like Octomap and Elevation Mapping.
The paper tackles the problem of local 3D environment representation for autonomous ground vehicle navigation by introducing OVPC Mesh, a watertight 3D mesh from point clouds that conservatively estimates free space, and shows it provides reliable 3D information for trajectory planning in real-time.
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.