Towards Packaging Unit Detection for Automated Palletizing Tasks
Markus Völk Kilian Kleeberger Werner Kraus Richard Bormann
For various automated palletizing tasks, the detection of packaging units is
a crucial step preceding the actual handling of the packaging units by an
industrial robot. We propose an approach to this challenging problem that is
fully trained on synthetically generated data and can be robustly applied to
arbitrary real world packaging units without further training or setup effort.
The proposed approach is able to handle sparse and low quality sensor data, can
exploit prior knowledge if available and generalizes well to a wide range of
products and application scenarios. To demonstrate the practical use of our
approach, we conduct an extensive evaluation on real-world data with a wide
range of different retail products. Further, we integrated our approach in a
lab demonstrator and a commercial solution will be marketed through an
industrial partner.