论文标题
高程状态空间：移动机器人不平衡环境中基于冲浪的导航
Elevation State-Space: Surfel-Based Navigation in Uneven Environments for Mobile Robots
论文作者
论文摘要
本文通过底层云的表面表示，在不平坦的环境中引入了一种新的机器人运动计划和导航的方法。所提出的方法通过将机器人的运动学和物理约束与标准运动计划算法（例如，来自开放运动计划库的那些）合并到了最先进的导航方法的缺点，从而使基于有效的采样计划的计划者能够在原始的点云映射上挑战基于采样的计划者。与基于数字高程图（DEM）的技术不同，我们的新型基于表面的状态空间公式和实现基于原始点云图，从而允许建模重叠的表面，例如桥梁，码头和隧道。实验结果证明了在真实和模拟的非结构化环境中提出的机器人导航方法的鲁棒性。拟议的方法还通过将其成功率提高到5倍，以挑战非结构化的地形计划和导航，从而优化了计划者的性能，这要归功于我们的基于Surfel的方法的机器人约束意识到的抽样策略。最后，我们提供了拟议方法的开源实施，以使机器人社区受益。
This paper introduces a new method for robot motion planning and navigation in uneven environments through a surfel representation of underlying point clouds. The proposed method addresses the shortcomings of state-of-the-art navigation methods by incorporating both kinematic and physical constraints of a robot with standard motion planning algorithms (e.g., those from the Open Motion Planning Library), thus enabling efficient sampling-based planners for challenging uneven terrain navigation on raw point cloud maps. Unlike techniques based on Digital Elevation Maps (DEMs), our novel surfel-based state-space formulation and implementation are based on raw point cloud maps, allowing for the modeling of overlapping surfaces such as bridges, piers, and tunnels. Experimental results demonstrate the robustness of the proposed method for robot navigation in real and simulated unstructured environments. The proposed approach also optimizes planners' performances by boosting their success rates up to 5x for challenging unstructured terrain planning and navigation, thanks to our surfel-based approach's robot constraint-aware sampling strategy. Finally, we provide an open-source implementation of the proposed method to benefit the robotics community.
