Robot-Centric Elevation Map Completion with Sensor Geometry-Aware Augmentation and Uncertainty Estimation
Robot-centric elevation maps built from onboard sensing are always incomplete: occlusions, a limited field of view, and range limits leave large unobserved regions that traversability analysis and motion planning must still reason about. We present a supervised framework that completes these maps and reports a per-cell uncertainty. Its core is a ray-cone augmentation that removes angular sectors anchored at the sensor origin during training; unlike the random masks of image inpainting, these sectors match the coverage gaps of real deployments, such as camera failures or reduced camera configurations. Partial maps generated from four depth cameras along legged-robot trajectories in the TartanGround dataset are paired with dense ground truth, yielding 32,329 samples across five outdoor environments. An encoder–decoder network is trained with a masked β-NLL loss and evaluated with a five-fold leave-one-environment-out protocol. The augmentation lowers the completion error on missing sensor sectors by 8.3 to 9.7%, depending on the sector width, at no measurable cost on uncorrupted partial inputs. The completed maps reach a hole root-mean-square error of 2.86 m, a 45% improvement over the strongest classical interpolation baseline.