Terrain Estimation for Off-Road Vehicles Using Gaussian Mixture Model

Off-road vehicles typically have to navigate very rough terrain environments. In the case of military off-road vehicles, terrain environments could be extreme. Accurately estimating terrain is critical for these vehicles' safe and efficient navigation. It is also essential for optimizing energy consumption and minimizing stress on the mechanical components. This paper provides a statistical model approach for terrain profile estimation, i.e., the Gaussian Mixture Model. The approach involves the observation of key data (terrain elevation (height), soil moisture content, stress at tire contact area, and soil particle size) for estimating the terrain profile. It uses the maximum likelihood estimation for mixtures of Gaussian models. We obtain the Gaussian mixture model parameters using the training data, which helps infer the most probable terrain profile from the test data. The simulation results provide the effectiveness and accuracy of the proposed method in the paper.