Fast and accurate path planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured outdoor environments. However, most existing methods exploiting either 2D or 2.5D maps struggle to balance the efficiency and safety for ground robots navigating in such challenging scenarios. In this paper, we propose a novel hybrid map representation by fusing a 2D grid and a 2.5D digital elevation map. Based on it, a novel path planning method is proposed, which considers the robot poses during traversability estimation. By doing so, our method explicitly takes safety as a planning constraint enabling robots to navigate unstructured environments smoothly.The proposed approach has been evaluated on both simulated datasets and a real robot platform. The experimental results demonstrate the efficiency and effectiveness of the proposed method. Compared to state-of-the-art baseline methods, the proposed approach consistently generates safer and easier paths for the robot in different unstructured outdoor environments. The implementation of our method is publicly available at https://github.com/nubot-nudt/T-Hybrid-planner.

翻译：快速精确的路径规划对于地面机器人在非结构化户外环境中实现安全高效的自主导航至关重要。然而，现有多数基于2D或2.5D地图的方法难以在这类挑战性场景中平衡地面机器人的导航效率与安全性。本文提出一种融合二维网格与2.5D数字高程地图的新型混合地图表征方法，并在此基础上提出一种在通行性估计中考虑机器人位姿的新型路径规划方法。该方法将安全显式作为规划约束条件，使机器人能够平滑穿越非结构化环境。该算法已在仿真数据集和真实机器人平台上完成评估，实验结果验证了其高效性与有效性。相较于现有基准方法，本方法能在不同非结构化户外环境中始终为机器人生成更安全、更易通行的路径。算法实现代码已在 https://github.com/nubot-nudt/T-Hybrid-planner 开源。