RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation

TL;DR

This paper introduces RAMP, a pipeline combining risk-aware mapping and planning for fast, safe off-road ground robot navigation in complex 3D terrains, improving map accuracy and planning speed.

Contribution

It presents novel mapping and planning methods, including ground point inflation with spatial memory and an MPPI-based planner with variable horizon, for enhanced safety and speed.

Findings

Accurate occupancy maps from pointclouds improve safety.

Variable horizon MPPI speeds up navigation.

Risk constraints enable safe, fast traversal.

Abstract

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.