TY - CHAP A1 - Arndt, Tobias A1 - Conzen, Max A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Galla, Oskar A1 - Köse, Hakan A1 - Schiffer, Stefan A1 - Tschesche, Matteo T1 - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers T2 - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments N2 - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products. KW - Datasets KW - Neural networks KW - Anomaly detection KW - Quality control KW - Process optimization Y1 - 2023 SN - 9798400700699 U6 - http://dx.doi.org/10.1145/3594806.3596558 N1 - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023. SP - 535 EP - 542 PB - ACM ER - TY - CHAP A1 - Dey, Thomas A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Frauenrath, Tobias A1 - Reke, Michael A1 - Schiffer, Stefan ED - Makedon, Fillia T1 - CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom T2 - PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference N2 - In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway. KW - embedded hardware KW - sensor networks KW - information systems KW - education KW - do-it-yourself Y1 - 2021 SN - 9781450387927 U6 - http://dx.doi.org/10.1145/3453892.3462697 N1 - PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference Corfu Greece 29 June 2021- 2 July 2021 SP - 292 EP - 299 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS Schooling Curricula via Contentual Taxonomies T2 - Robotics in Education Y1 - 2021 SN - 978-3-030-67411-3 U6 - http://dx.doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - CHAP A1 - Eltester, Niklas Sebastian A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - A smart factory setup based on the RoboCup logistics league T2 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) N2 - In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs. Y1 - 2020 U6 - http://dx.doi.org/10.1109/ICPS48405.2020.9274766 N1 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 10-12 June 2020, Tampere, Finland. SP - 297 EP - 302 PB - IEEE ER - TY - CHAP A1 - Kirsch, Maximilian A1 - Mataré, Victor A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - Integrating golog++ and ROS for Practical and Portable High-level Control T2 - 12th International Conference on Agents and Artificial Intelligence Y1 - 2020 U6 - http://dx.doi.org/10.5220/0008984406920699 ER - TY - CHAP A1 - Reke, Michael A1 - Peter, Daniel A1 - Schulte-Tigges, Joschua A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Walter, Thomas A1 - Matheis, Dominik T1 - A Self-Driving Car Architecture in ROS2 T2 - 2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa Y1 - 2020 SN - 978-1-7281-4162-6 U6 - http://dx.doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020 SP - 1 EP - 6 ER - TY - JOUR A1 - Limpert, Nicolas A1 - Wiesen, Patrick A1 - Ferrein, Alexander A1 - Kallweit, Stephan A1 - Schiffer, Stefan T1 - The ROSIN Project and its Outreach to South Africa JF - R&D Journal Y1 - 2019 VL - 35 SP - 1 EP - 6 ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - An Optimized Method for 3D Body Scanning Applications Based on KinectFusion T2 - Communications in Computer and Information Science Y1 - 2019 U6 - http://dx.doi.org/10.1007/978-3-030-29196-9_6 SN - 1865-0929 N1 - 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018; Funchal; Portugal; 19 January 2018 through 21 January 2018 VL - 1024 SP - 100 EP - 113 PB - Springer ER - TY - JOUR A1 - Bartella, Alexander K. A1 - Kamal, Mohammad A1 - Scholl, Ingrid A1 - Schiffer, Stefan A1 - Steegmann, Julius A1 - Ketelsen, Dominik A1 - Hölzle, Frank W. A1 - Lethaus, Bernd T1 - Virtual reality in preoperative imaging in maxillofacial surgery: implementation of “the next level”? JF - British Journal of Oral and Maxillofacial Surgery Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.bjoms.2019.02.014 SN - 0266-4356 VL - 57 IS - 7 SP - 644 EP - 648 PB - Elsevier CY - Amsterdam ER -