Dense Traversability Estimation System for Extreme Environments

Abstract

Traversability estimation is essential for safe path planning in robotics and autonomous driving. In this study, we propose an accurate and dense traversability estimation system for extreme environments such as disaster areas. Traversability estimations often occur undefined regions due to the shielding and sparsity of sensor data. These regions may lead to the selecting of dangerous paths. The proposed system uses point cloud accumulation and the Bayesian generalized kernel (BGK) elevation estimation to create a dense Digital elevation map (DEM) with no undefined regions from point clouds obtained from light detection and ranging (LiDAR). Subsequently, traversability is estimated from road surface roughness, slope and vehicle performance using fuzzy logic in our system. In our experiments, our system is installed in an experimental vehicle, and the experiments conducted on rocky terrain, slopes, and craters to verify its effectivity in extreme environments. Results show that our system can create a dense DEM with few undefined regions and estimate valid traversability for all obstacles.