论文标题
3D越野地形的基于学习的不确定性意识到导航
Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains
论文作者
论文摘要
本文为3D越野地形环境提供了一种安全，高效且敏捷的地面车导航算法。越野导航受到3D地形拓扑结构不同地形条件引起的不确定的车辆 - 透水相互作用。现有的作品仅限于采用过于简化的车辆模型。拟议的算法从驱动数据中了解了地形引起的不确定性，并将学习的不确定性分布编码到路径评估的遍历成本中。然后，设计导航路径以优化不确定性感知的遍历成本，从而导致安全敏捷的车辆操纵。确保实时执行，该算法将在图形处理单元（GPU）上运行的并行计算体系结构中进一步实现。
This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicle-terrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU).
