Real-time Cargo Volume Recognition using Internet-connected 3D Scanners

Abstract

Transport and logistics faces fluctuations in cargo volume that statistically can only be captured with a large error. Observing and managing such dynamic volume fluctuations more effectively promises many benefits such as reducing unused transport capacity and ensuring timely delivery of cargo. This paper introduces an approach that combines user-friendly mobile devices with internet-connected sensors to deliver up-to-date, timely, and precise information about parcel volumes inside containers. In particular, we present (1) RCM, a mobile app for unique identification of containers, and (2) SNAP, a novel approach for employing internet-connected low-cost, off-the-shelf 3D scanners for capturing and analyzing actual cargo volumes. We have evaluated the accuracy of SNAP in controlled experiments indicating that cargo volume can be measured with high accuracy. We have further evaluated RCM together with SNAP by means of a survey study with domain experts, revealing its high potential for practical use.