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, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not consider speeds in known free and unknown space separately, leading to slower overall plans. The RAMP pipeline proposed here solves these issues using new mapping and planning methods. This work first presents ground point inflation with persistent spatial memory 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 hardware demonstrations.
翻译：在3D地形快速地面机器人导航方面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,通过射线追踪生成2D占用网格的普遍做法使得生成的地图由于不明空间的不准确代表而无法进行规划。此外,如移动电话伙伴关系倡议等现有规划人员并不考虑已知自由空间和未知空间的速度,从而导致总体计划缓慢。在此提议的RAMP管道使用新的绘图和规划方法解决这些问题。这项工作首先展示地点通货膨胀,用持久性空间内存作为从分类的圆锥体生成准确占用网格图的一种方法。然后,我们展示一个基于移动电话伙伴关系的图案规划员,该图案在已知自由空间上尽可能加快速度,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进行快速和安全的导航。</s>
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