Towards Packaging Unit Detection for Automated Palletizing Tasks
This addresses the challenge of robust packaging unit detection for industrial automation, though it appears incremental as it builds on synthetic training methods.
The paper tackles the problem of detecting packaging units for automated palletizing by proposing a method fully trained on synthetic data, which generalizes to real-world products without additional training, as demonstrated through extensive evaluation on diverse retail products.
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.