Support Surface Estimation for Legged Robots

Abstract

The high agility of legged systems allows them to operate in rugged outdoor environments. In these situations, knowledge about the terrain geometry is key for foothold planning to enable safe locomotion. However, on penetrable or highly compliant terrain (e.g. grass) the visibility of the supporting ground surface is obstructed, i.e. it cannot directly be perceived by depth sensors. We present a method to estimate the underlying terrain topography by fusing haptic information about foot contact closure locations with exteroceptive sensing. To obtain a dense support surface estimate from sparsely sampled footholds we apply Gaussian process regression. Exteroceptive information is integrated into the support surface estimation procedure by estimating the height of the penetrable surface layer from discrete penetration depth measurements at the footholds. The method is designed such that it provides a continuous support surface estimate even if there is only partial exteroceptive information available due to shadowing effects. Field experiments with the quadrupedal robot ANYmal show how the robot can smoothly and safely navigate in dense vegetation.