Exploring Load Localization for Automated Guided Vehicles: Pallet pose estimation using a depth sensing camera

Abstract

Automated guided vehicles (AGVs) typically require pallets to be placed in precise locations to be able to pick them without advanced detection systems. This limits AGV systems in environments where pallets are also handled by human fork lift operators. Modern solutions to this problem often rely on machine learning, which requires powerful and costly computers inside the AGVs. In this thesis a computationally efficient method for estimating the pose of a pallet using a depth sensing camera is proposed and evaluated. The method uses a novel approach to find the region of interest directly in the depth map by segmenting it into depth slices and detecting the fork pockets of the pallet with fast 2D image processing techniques. A rough pose estimate is calculated from the detected fork pockets, after which only a small region around the pallet is deprojected into a point cloud where the pose is refined using the iterative closest point (ICP) algorithm. The method was evaluated on 2678 depth maps of EU-pallets captured on the floor and in a rack at angles up to ±15◦. A pallet was detected in 90.14% of the frames and 73.61% of the frames resulted in an accepted pose estimation. The standard deviation of the estimated vertical position of a stationary pallet was 4.59 mm in the best case, indicating a high repeatability. The complete pipeline was 30.32% faster than performing ICP on the full point cloud, showing that the proposed approach reduces the computational load while maintaining an accurate localization.