  Estimating the pose of a pallet and other logistics objects is crucial for
various use cases, such as automatized material handling or tracking.
Innovations in computer vision, computing power, and machine learning open up
new opportunities for device-free localization based on cameras and neural
networks. Large image datasets with annotated poses are required for training
the network. Manual annotation, especially of 6D poses, is an extremely
labor-intensive process. Hence, newer approaches often leverage synthetic
training data to automatize the process of generating annotated image datasets.
In this work, the generation of synthetic training data for 6D pose estimation
of pallets is presented. The data is then used to train the Deep Object Pose
Estimation (DOPE) algorithm. The experimental validation of the algorithm
proves that the 6D pose estimation of a standardized Euro pallet with a
Red-Green-Blue (RGB) camera is feasible. The comparison of the results from
three varying datasets under different lighting conditions shows the relevance
of an appropriate dataset design to achieve an accurate and robust
localization. The quantitative evaluation shows an average position error of
less than 20 cm for the preferred dataset. The validated training dataset and a
photorealistic model of a Euro pallet are publicly provided.
