Options
Posenschätzung einer Europalette mit einer RGB-Kamera basierend auf synthetischen Trainingsdaten
Publikationstyp
Journal Article
Publikationsdatum
2022
Sprache
German
Author
Institut
Enthalten in
Volume
2022
Issue
11
Citation
Logistics Journal 11: (2022)
Publisher DOI
Scopus ID
E stimating 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 [1]. 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 [2].
Schlagworte
6D pose estimation
DOPE algorithm
Euro pallet
RGB camera
synthetic training dataset