Robust Pallet Detection for Automated Logistics Operations

A pallet detection system is presented which is designed for automated forklifts for logistics operations. The system performs stereo reconstruction and pallets are detected using a sliding window approach. In this paper we propose a candidate generation method and we introduce feature descriptors for grayscale images that are tailored to the current task. The features are designed to be invariant to certain types of illumination changes and are called normalized pair differences because of the formula involved in their calculation. Experimental results validate our approach on extensive real world data.