Towards Packaging Unit Detection for Automated Palletizing Tasks

TL;DR

This paper presents a robust, synthetic-data-trained method for packaging unit detection in automated palletizing, capable of generalizing to real-world scenarios without additional training.

Contribution

It introduces a novel approach that leverages synthetic data for training and generalizes well to real-world packaging units in industrial applications.

Findings

Effective on diverse retail products

Handles sparse and low-quality sensor data

Generalizes without further training

Abstract

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.