Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning

This paper presents a self-supervised learning based method to reconstruct the occluded elevation map. Published in: IEEE Robotics and Automation Letters ( Volume: 7, Issue: 2, April 2022) IEEE Xplore: arXiv: Code: Abstract: Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing values in the elevation map are filled-in using traditional interpolation, diffusion or patch-matching techniques. These methods cannot leverage the high-level terrain characteristics and the geometric constraints of line of sight we humans use intuitively to predict occluded areas. We introduce a self-supervised learn