Vision-Based Pallet Detection and Load Height Estimation for Autonomous Material Handling Systems

As logistics and warehouse management evolve, the demand for accurate and real-time information on pallets and their load heights grows. In addition, a shortage of skilled employees demands an ergonomic work environment. This paper introduces a novel approach for the automated detection of pallets and estimation of load height using a depth camera system, leveraging computer vision techniques to analyze depth information. The system facilitates accurate estimation of the pallet’s load height, enabling autonomous vehicles with lift tables to automatically adjust to an ergonomic work height. The developed approach demonstrates promising results in efficiency and achieves subcentimeter accuracy, offering potential applications in logistics, warehouse management, and industrial automation.