@inproceedings{FerreinSchollNeumannetal.2019,
  author    = {Ferrein, Alexander and Scholl, Ingrid and Neumann, Tobias and Kr{\"u}ckel, Kai and Schiffer, Stefan},
  title     = {A system for continuous underground site mapping and exploration},
  doi       = {10.5772/intechopen.85859},
  pages     = {16 Seiten},
  year      = {2019},
  language  = {en}
}
@inproceedings{SchifferFerrein2017,
  author    = {Schiffer, Stefan and Ferrein, Alexander},
  title     = {A System Layout for Cognitive Service Robots},
  series = {Cognitive Robot Architectures. Proceedings of EUCognition 2016},
  booktitle = {Cognitive Robot Architectures. Proceedings of EUCognition 2016},
  issn      = {1613-0073},
  pages     = {44 -- 45},
  year      = {2017},
  language  = {en}
}
@inproceedings{EvansBraunUlmeretal.2022,
  author    = {Evans, Benjamin and Braun, Sebastian and Ulmer, Jessica and Wollert, J{\"o}rg},
  title     = {AAS implementations - current problems and solutions},
  series = {20th International Conference on Mechatronics - Mechatronika (ME)},
  booktitle = {20th International Conference on Mechatronics - Mechatronika (ME)},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {978-1-6654-1040-3},
  doi       = {10.1109/ME54704.2022.9982933},
  pages     = {6 Seiten},
  year      = {2022},
  abstract  = {The fourth industrial revolution presents a multitude of challenges for industries, one of which being the increased flexibility required of manufacturing lines as a result of increased consumer demand for individualised products. One solution to tackle this challenge is the digital twin, more specifically the standardised model of a digital twin also known as the asset administration shell. The standardisation of an industry wide communications tool is a critical step in enabling inter-company operations. This paper discusses the current state of asset administration shells, the frameworks used to host them and their problems that need to be addressed. To tackle these issues, we propose an event-based server capable of drastically reducing response times between assets and asset administration shells and a multi-agent system used for the orchestration and deployment of the shells in the field.},
  language  = {en}
}
@inproceedings{UlmerBraunChengetal.2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Adapting augmented reality systems to the users' needs using gamification and error solving methods},
  series = {Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
  volume    = {104},
  booktitle = {Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2021.11.024},
  pages     = {140 -- 145},
  year      = {2021},
  abstract  = {Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users' preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.},
  language  = {en}
}
@inproceedings{MarcoFerrein2017,
  author    = {Marco, Heather G. and Ferrein, Alexander},
  title     = {AGNES: The African-German Network of Excellence in Science},
  series = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017},
  booktitle = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017},
  pages     = {1 -- 2},
  year      = {2017},
  language  = {en}
}
@inproceedings{AlhwarinFerreinScholl2019,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {An Efficient Hashing Algorithm for NN Problem in HD Spaces},
  series = {Lecture Notes in Computer Science},
  booktitle = {Lecture Notes in Computer Science},
  isbn      = {978-303005498-4},
  doi       = {10.1007/978-3-030-05499-1_6},
  pages     = {101 -- 115},
  year      = {2019},
  language  = {en}
}
@inproceedings{ZugNiemuellerHochgeschwenderetal.2017,
  author    = {Zug, Sebastian and Niemueller, Tim and Hochgeschwender, Nico and Seidensticker, Kai and Seidel, Martin and Friedrich, Tim and Neumann, Tobias and Karras, Ulrich and Kraetzschmar, Gerhard K. and Ferrein, Alexander},
  title     = {An Integration Challenge to Bridge the Gap Among Industry-Inspired RoboCup Leagues},
  series = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.},
  booktitle = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-68792-6},
  doi       = {10.1007/978-3-319-68792-6_13},
  pages     = {157 -- 168},
  year      = {2017},
  language  = {en}
}
@inproceedings{EngemannWiesenKallweitetal.2018,
  author    = {Engemann, Heiko and Wiesen, Patrick and Kallweit, Stephan and Deshpande, Harshavardhan and Schleupen, Josef},
  title     = {Autonomous mobile manipulation using ROS},
  series = {Advances in Service and Industrial Robotics},
  booktitle = {Advances in Service and Industrial Robotics},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-61276-8},
  doi       = {10.1007/978-3-319-61276-8_43},
  pages     = {389 -- 401},
  year      = {2018},
  language  = {en}
}
@incollection{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and Ning, Chuanfang and Anwar, Saqib},
  title     = {AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation},
  series = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  booktitle = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  publisher = {IOS Press},
  address   = {Amsterdam},
  isbn      = {978-1-64368-137-5},
  doi       = {10.3233/FAIA200770},
  pages     = {89 -- 97},
  year      = {2020},
  abstract  = {We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.},
  language  = {en}
}
@article{SchulteTiggesFoersterNikolovskietal.2022,
  author    = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas},
  title     = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s22197146},
  pages     = {20 Seiten},
  year      = {2022},
  abstract  = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.},
  language  = {en}
}