  Volume estimation in large indoor spaces is an important challenge in robotic
inspection of industrial warehouses. We propose an approach for volume
estimation for autonomous systems using visual features for indoor localization
and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based
model incorporates information collected from measurements given statistical
prior information about the terrain, from which the volume estimate is
computed. Our algorithm finds feasible trajectories which minimize the
uncertainty of the volume estimate. We show results in simulation for the
surface reconstruction and volume estimate of topographic data.
