Detecting airport luggage dimensions through low-cost depth sensors

Abstract

A factor that impacts airlines' resources is the verification of luggage’s dimensions during the boarding process. Companies often rely on a human operator to perform this check using a manual template, which can cause delays. As an alternative, companies are investing in self bag drop systems. This process introduces new technological challenges since, in this scenario, checking the conformity of luggage dimensions is delegated to the passenger, which can lead to errors. In addition, current solutions use specific computational devices, such as laser scanners, that are expressive in size and cost, which may require interventions in the airport infrastructure. To overcome this, isolated initiatives are observed with alternative technologies, such as low-cost depth sensors, but they usually come without a scientific investigation. In this sense, this work investigates the technical viability of using such low-cost devices to obtain the dimensions of airport baggage. To do so, we developed a model that obtains a 3D point cloud of the luggage surface through a Microsoft Kinect V2 sensor. This cloud is treated and processed to extract the dimensions of the luggage. In order to validate this approach, a full-scale physical prototype was built and tested. The results indicate that the mean absolute error of the obtained dimension by the proposed model is 2.86 cm. Such data suggest that this technology has the potential to become an alternative to detect the dimensions of airport luggage.