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：三维地形中地面机器人快速导航的关键挑战在于平衡速度与安全性。近期研究表明，2.5维地图（带有附加三维信息的二维表示）是实现实时安全快速规划的理想选择。然而，当前普遍采用的通过光线投射生成二维占据栅格的方法，因其对未知空间的不准确表征，导致生成的地图难以安全规划。此外，现有规划器（如MPPI）未能分别考虑已知自由空间与未知空间中的速度，致使整体规划速度较慢。本文提出的RAMP流程通过创新的地图构建与规划方法解决了上述问题。首先，我们提出具有持久空间记忆的地面点膨胀方法，以从分类点云生成精确的占据栅格地图；随后，提出一种基于MPPI的规划器，其规划时域内嵌可变性，在已知自由空间中最大化速度，同时保持对未知空间的谨慎渗透；最后，我们将该地图构建与规划流程与三维地形引发的风险约束相整合，并通过仿真与硬件实验验证其能够实现快速、安全的导航。