3D OBJECT CATEGORIZATION OF LOGISTIC GOODS FOR AUTOMATED HANDLING

The automated handling of universal logistic goods through robotic systems requires suitable and reliable methods for categorizing different logistic goods. They must be able to detect the pose of different types and sizes of logistic goods in order to identify possible gripping points or for selecting a suitable gripping system for the detected object type. For this purpose, Time-of-Flight or Structured Light sensors can deliver a dense 3D representation of the investigated scenario. This paper presents a 3D object categorization system for logistic goods based on synthetically generated model data. We generate the model data by using a sensor simulation framework for different TOF-sensor types. The framework creates point clouds of self-defined geometric models of logistic goods or CAD data. Afterwards, we use these synthetic point clouds for generating a suitable model database offline. In order to evaluate our approach, we describe the synthetic point clouds by global point feature description techniques to distinguish between different types of logistic goods. Finally, we evaluate our concept with real sensor data from different logistic goods.