Hendrik Thamer

A 3D-robot vision system for automatic unloading of containers

Unloading of standard containers within logistic processes is mainly performed manually. Amongst gripping technology, the development of a robot vision system for recognizing different shaped logistic goods is a major technical obstacle for developing robotic systems for automatic unloading of containers. Goods can be arbitrarily placed inside a container and the resulting packaging scenarios usually have a high degree of occlusion. Existing systems and approaches use range information acquired by laser scanners for recognizing and localizing goods inside of containers. They are restricted to a single shape class of goods and often have limited size ranges for goods. This paper presents a robot vision for recognizing and localizing differently shaped and sized objects in piled packaging scenarios using range data acquired by different kinds of range sensors. After a specific segmentation step, different shaped partial surfaces are detected and classified in point cloud data and combined to complete logistic goods. The system is evaluated with real and simulated sensor data from different packaging scenarios.

Automated Surface Inspection of Micro Parts

This chapter presents a machine vision system for detecting surface imperfections on micro parts. It is part of a quality control concept for micro production. Because of increasing product miniaturization, the mechanical manufacturing of micro components is becoming more and more important. The combination of high manufacturing rates and low tolerances in manufacturing processes enables the economical production of micro components. Due to the small component sizes and the difficulties associated with the handling process, the manual visual inspection retires as testing procedure. A customized surface inspection technology with an efficient image processing and classification system is needed. The objective of our concept is to identify surface imperfections such as raisings, laps and bulges on micro parts. The implementation of the system is explained by reference to a micro deep‐drawn component, which is manufactured within the German Collaborative Research Center (CRC) 747.

3D-Computer Vision for Automation of Logistic Processes

The availability of low-cost range sensors has led to several innovative implementations and solutions in various application fields like object recognition and localization, scene understanding, human-robot interaction or measurement of objects. The transfer of the corresponding methods and techniques to logistic processes needs the consideration of specific requirements. A logistic application field that requires robust and reliable 3D vision systems is automated handling of universal logistic goods for (de-)palletizing or unloading of standard containers in the field of sea and air cargo. This paper presents a 3D-computer vision system for recognizing and localizing different shaped logistic goods for automated handling by robotic systems. The objective is to distinguish between different types of goods like boxes, barrels or sacks due to their geometric shape in point cloud data. The system is evaluated with sensor data from a low-cost range sensor and ideal simulated data representing different shaped logistic goods as well.

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.

Design of SmartGate Technologies for Enhanced Material Handling

The use of information technologies in logistic processes leads to higher automation and efficiency. Nevertheless, information of cargo is often incomplete or incorrect. This affects the material handling processes in planning as well as in operation. This paper presents a SmartGate approach, which contains multiple technologies for identification and exploration of goods for enhanced material handling. This includes identification as well as optic and haptic exploration technologies. The idea is to have a system, which gathers all available information of a good by use of non-destructive testing methods. Besides the optic and haptic exploration technologies, a feasible material handling system and the utilization of the gained information for load planning is shown.

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.

Klassifikation von Oberflächenunvollkommenheiten in der Mikrokaltumformung

Kurzfassung Die mechanische Fertigung von Mikrobauteilen im Subfeinwerkbereich gewinnt durch die zunehmende Miniaturisierung in allen Bereichen der Technik kontinuierlich an Bedeutung. Die Kombination von hohen Fertigungsraten und niedrigen Fertigungstoleranzen im Mikrometerbereich erfordert dabei eine umfassende Qualitätssicherung, welche Gutteile von Schlechtteilen unterscheidet und Rückmeldung im Sinne der Qualitätsregelung gibt. Auf Grund der geringen Bauteilgröße und der damit verbundenen schwierigen Handhabung bedarf es einer Kombination von auf die Mikrofertigung angepasster Messtechnik und leistungsfähiger Bildverarbeitungssysteme. Im Fokus dieses Beitrags stehen die Identifikation und die Klassifikation von Oberflächenunvollkommenheiten nach DIN EN ISO 8785 mittels Bildverarbeitung. Anhand eines kaltumgeformten Mikrobauteils aus dem Sonderforschungsbereich (SFB) 747 „Mikrokaltumformen“ wird die prototypische Implementierung des Bildverarbeitungssystems vorgestellt und die Detektion von Oberflächenunvollkommenheiten in 3D-Tiefenbildern beschrieben. In einem zweiten Schritt wird exemplarisch die Eignung unterschiedlicher Klassifikationsverfahren zur Klassifizierung von Oberflächenunvollkommenheiten nach DIN EN ISO 8785 anhand synthetisch erzeugter Fehlerbilder für fünf Klassen untersucht.