Abstract

Different algorithms for object dimension measurement have recently been proposed, leveraging the power of RGB-D cameras. However, most of these algorithms are not suitable for deployment in the logistics industry: they are only able to deal with non-moving objects and/or the usage of multiple cameras introduces time delays. In this paper, we introduce Box-Scan, a novel algorithm that enables real-time box dimension measurement in conveyor systems using a single RGB-D camera. We discuss the industrial setting in which our algorithm needs to operate, as well as a prototype that integrates the proposed algorithm. Furthermore, we provide an analysis of the effectiveness of our prototype as a function of the conveyor speed, demonstrating that the prototype built comes with a maximum measurement error of less than 5% at a conveyor speed of 3.4 km/h.

Highlights

  • The logistics industry uses boxes for protecting, loading, and packaging a plethora of items

  • The first step deploys a Region-ofInterest (ROI) processing procedure, only extracting those parts from the depth frames of the RGB-D camera that are necessary for performing box dimension measurement, making it possible to significantly reduce the computational complexity

  • In order to run Algorithm 1 for box width and height measurement, we propose the use of Algorithm 2, a high-level algorithm for system state control

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Summary

Introduction

The logistics industry uses boxes for protecting, loading, and packaging a plethora of items. Boxes have been designed to protect their content, allowing for deformations in order to absorb the impact of shocks This implies that a certain error tolerance is allowed when performing dimension measurement. We can argue that the use of high-speed and high-precision equipment for box dimension measurement is not cost effective in a logistics setting. In this paper, we present Box-Scan, a hierarchical algorithm that can perform box dimension measurement on a conveyor in real time, making use of a single RGB-D camera, a common desktop computer, and some fixing equipment. The first step deploys a Region-ofInterest (ROI) processing procedure, only extracting those parts from the depth frames of the RGB-D camera that are necessary for performing box dimension measurement, making it possible to significantly reduce the computational complexity.

RGB-D cameras and depth frames
RGB-D cameras for dimension measurement
Proposed Approach
ROI processing
Implementation
Prototype description
Prototype visualization
Performance evaluation
Findings
Conclusions and directions for future work

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