Drone-based Volume Estimation in Indoor Environments

TL;DR

This paper presents a novel drone-based method for estimating the volume of large indoor spaces using visual features, surface reconstruction, and Gaussian Process models to improve accuracy and reduce uncertainty.

Contribution

It introduces an integrated approach combining visual localization, surface reconstruction, and probabilistic modeling for autonomous indoor volume estimation.

Findings

Successful simulation results for surface reconstruction

Accurate volume estimation with reduced uncertainty

Feasible trajectories for minimal estimation error

Abstract

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.