Terrain-aware autonomous robot navigation in outdoor environments

Abstract

In this work, we propose a method to perceive a terrain's geometric and other surface properties for efficient motion planning in outdoor environments. Our method incorporates two perception branches to identify the terrain's elevation and roughness separately. The first branch uses an elevation map created using LiDAR point clouds to compute a cost map that represents critical elevation changes. The second branch uses a vision-based cost map trained using RGB images, IMU, and robot odometry. Then, least-cost waypoints are calculated on a combined cost map and are followed using the Dynamic Window Approach (DWA). Our planner navigates along the least-cost waypoints while adaptively varying the velocities to reduce vibration. We evaluate our method's performance on a Husky robot in real-world environments. We observe that our method leads to higher success rates, and lower vibrations compared to state-of-the-art methods.