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Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning by Stölzle, Maximilian; Miki, Takahiro; Gerdes, Levin; Azkarate, Martin; Hutter, Marco is a scholarly article available to read on EtoBox.

What is Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning about?

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 learning approach capable of training on real-world data without a need for ground-truth information to reconstruct the occluded areas in the DEMs. We accomplish this by adding artificial occlusion to the incomplete elevation maps constructed on a real robot by performing ray casting. We first evaluate a supervised learning approach on synthetic data for which we have the full ground-truth available and subsequently move to several real-world datasets. These real-world datasets were recorded during exploration of both structured and unstruc

Author

Stölzle, Maximilian; Miki, Takahiro; Gerdes, Levin; Azkarate, Martin; Hutter, Marco

Published

2021

Language

EN