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.
@article{arxiv.2211.08013,
title = {Drone-based Volume Estimation in Indoor Environments},
author = {Samuel Balula and Dominic Liao-McPherson and Stefan Stevšić and Alisa Rupenyan and John Lygeros},
journal= {arXiv preprint arXiv:2211.08013},
year = {2022}
}