Real-time path planning in outdoor environments still challenges modern robotic systems due to differences in terrain traversability, diverse obstacles, and the necessity for fast decision-making. Established approaches have primarily focused on geometric navigation solutions, which work well for structured geometric obstacles but have limitations regarding the semantic interpretation of different terrain types and their affordances. Moreover, these methods fail to identify traversable geometric occurrences, such as stairs. To overcome these issues, we introduce ViPlanner, a learned local path planning approach that generates local plans based on geometric and semantic information. The system is trained using the Imperative Learning paradigm, for which the network weights are optimized end-to-end based on the planning task objective. This optimization uses a differentiable formulation of a semantic costmap, which enables the planner to distinguish between the traversability of different terrains and accurately identify obstacles. The semantic information is represented in 30 classes using an RGB colorspace that can effectively encode the multiple levels of traversability. We show that the planner can adapt to diverse real-world environments without requiring any real-world training. In fact, the planner is trained purely in simulation, enabling a highly scalable training data generation. Experimental results demonstrate resistance to noise, zero-shot sim-to-real transfer, and a decrease of 38.02% in terms of traversability cost compared to purely geometric-based approaches. Code and models are made publicly available: https://github.com/leggedrobotics/viplanner.

翻译：户外环境中的实时路径规划仍对现代机器人系统构成挑战，原因包括地形可通行性差异、多样化障碍物以及快速决策的必要性。现有方法主要集中于几何导航解决方案，这类方案对结构化几何障碍物效果良好，但在不同地形类型的语义解释及其可供性方面存在局限。此外，这些方法无法识别可通行的几何结构（如楼梯）。为克服这些问题，我们提出ViPlanner——一种基于几何与语义信息生成局部路径规划的学习型局部路径规划方法。该系统采用指令学习范式进行训练，通过网络权重端到端优化实现规划任务目标。该优化过程使用可微分语义代价地图公式，使规划器能够区分不同地形的可通行性并精确识别障碍物。语义信息通过RGB色彩空间以30个类别进行表征，可有效编码多级可通行性。实验表明该规划器能适应多样化的真实环境，且无需任何真实世界训练。实际上，规划器完全在仿真环境中训练，实现了高度可扩展的训练数据生成。实验结果证明该方法具有抗噪性、零样本仿真到真实迁移能力，与纯几何方法相比可通行性成本降低38.02%。代码与模型已开源：https://github.com/leggedrobotics/viplanner。