We propose a robust and efficient framework to generate global trajectories for ground robots in complex 3D environments. The proposed method takes point cloud as input and efficiently constructs a multi-level map using triangular patches as the basic elements. A kinematic path search is adopted on the patches, where motion primitives on different patches combine to form the global min-time cost initial trajectory. We use a same-level expansion method to locate the nearest obstacle for each trajectory waypoint and construct an objective function with curvature, smoothness and obstacle terms for optimization. We evaluate the method on several complex 3D point cloud maps. Compared to existing methods, our method demonstrates higher robustness to point cloud noise, enabling the generation of high quality trajectory while maintaining high computational efficiency. Our code will be publicly available at https://github.com/ck-tian/MLMC-planner.

翻译：本文提出一种鲁棒且高效的框架，用于在复杂三维环境中为地面机器人生成全局轨迹。所提方法以点云作为输入，以三角面片为基本单元高效构建多层次地图。在面片层采用运动学路径搜索，通过不同面片上的运动基元组合形成全局最小时间成本的初始轨迹。我们使用同层扩展方法定位各轨迹航点的最近障碍物，并构建包含曲率、平滑度与障碍物约束的目标函数进行优化。该方法在多个复杂三维点云地图中进行了验证。与现有方法相比，本方法对点云噪声具有更强的鲁棒性，能够在保持高计算效率的同时生成高质量轨迹。代码已开源：https://github.com/ck-tian/MLMC-planner。