Combined Categorization and Localization of Logistic Goods Using Superquadrics

The detection and pose estimation of various shaped objects in real world cluttered application scenarios is a major technical challenge due to noisy sensor data and possible occlusions. Usually, a predefined model database is utilized for implementing a robust and reliable object detection system. Geometric models based on superquadrics have shown great potential and flexibility for representing a variety of shapes by using only a few parameters. In this paper, we propose a novel method concerning superquadric based Segment-then-fit approach and evaluate it in a logistic application scenario. The method utilizes boundary and region information to recover different types of convex shaped logistic goods in cluttered scenarios for automated handling by means of unorganized point cloud data. We have evaluated our approach using synthetic and multiple real sensor data on several packaging scenarios with various shaped logistic goods.