A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, raytracing as a prevalent method of generating occupancy grid as the base 2D representation makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not reason about speeds in known free and unknown space separately, leading to slow plans. This work therefore first presents ground point inflation as a way to generate accurate occupancy grid maps from classified pointclouds. Then we present an MPPI-based planner with embedded variability in horizon, to maximize speed in known free space while retaining cautionary penetration into unknown space. Finally, we integrate this mapping and planning pipeline with risk constraints arising from 3D terrain, and verify that it enables fast and safe navigation using simulations and a hardware demonstration.
翻译：3D地形快速地面机器人导航的一个关键挑战是平衡机器人的速度和安全。最近的工作表明,2.5D地图(2D图示加上额外的3D信息)是实时安全和快速规划的理想方法。然而,由于基2D图示作为一种常用的生成占用网格的方法,光学使生成的地图无法进行规划,因为未知空间的描述不准确。此外,移动电话伙伴关系举措等现有规划人员不理会已知自由空间和未知空间的单独速度,导致计划缓慢。因此,这项工作首先提出了地面点通胀,作为从分类的点云中生成准确的占用网格图的一种方式。然后,我们提出了一个基于移动电话伙伴关系伙伴关系的规划器,其地平面上嵌入变异性,以最大限度地提高已知自由空间的速度,同时保持谨慎地进入未知空间。最后,我们将这一绘图和规划管道与3D地形的风险限制结合起来,并核实它是否能够利用模拟和硬件演示实现快速和安全的导航。
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