SEM-RRT*: Fast Risk Assessment and Path Planning in Uneven Terrain using Statistical Elevation Map

Abstract

Path planning in uneven terrain scenarios is one of the core capabilities of intelligent off-road robots and vehicles. The complex terrain undulations often cause bumpy motion and sharp turns along the planned path, making smooth and safe path planning challenging. Most path planning methods in this community rely on dense point cloud maps as direct inputs, which inevitably incur high computational overhead for map representation and terrain assessment. To address these problems, we propose a novel path planning method toward uneven terrains, SEM-RRT*, which balances both planning quality and computational efficiency. First, we propose a map representation namely Statistical Elevation Map (SEM), which is lightweight to store and compute. Then, to enable fast terrain risk assessment, a terrain risk filter with omnidirectional and multi-scale characteristics is designed. Finally, we incorporate multi-objective cost evaluation, backward search, and rolling optimization strategies into the Informed RRT* framework, leveraging its path optimality on large scale map. Extensive experiments in challenging terrain scenarios, such as hills, canyons, and volcanic landscapes, show that SEM-RRT* outperforms existing methods in both path quality and computational time.