用三角形网格构建多层级地图，高效生成3D环境下的最优轨迹
Efficient Trajectory Generation in 3D Environments with Multi-Level Map Construction
- 以三角形片为基本单元构建多层级地图，提升复杂环境建模能力
- 在不同片上组合运动基元，生成最小时间代价的初始轨迹
- 兼顾曲率、平滑与避障，适合高动态地面机器人路径规划
我们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。该方法以点云为输入，利用三角形片作为基本元素，高效构建多层级地图。在三角形片上采用运动学路径搜索，通过不同片上的运动基元组合生成最小时间成本的初始轨迹。采用同级扩展法为每个轨迹航点定位最近障碍物，并构建包含曲率、平滑性和障碍物项的优化目标函数。我们在多个复杂3D点云地图上评估该方法，相比现有方法，在点云噪声下表现出更高鲁棒性，可在保持高计算效率的同时生成高质量轨迹。代码将公开于 https://github.com/ck-tian/MLMC-planner。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。