@inproceedings{FerreinMaierMuehlbacheretal.2016,
  author    = {Ferrein, Alexander and Maier, Christopher and M{\"u}hlbacher, Clemens and Niem{\"u}ller, Tim and Steinbauer, Gerald and Vassos, Stravros},
  title     = {Controlling logistics robots with the action-based language YAGI},
  series = {Intelligent Robotics and Applications: 9th International Conference, ICIRA 2016, Tokyo, Japan, August 22-24, 2016, Proceedings, Part I},
  volume    = {9834},
  booktitle = {Intelligent Robotics and Applications: 9th International Conference, ICIRA 2016, Tokyo, Japan, August 22-24, 2016, Proceedings, Part I},
  publisher = {Springer},
  isbn      = {978-3-319-43505-3 (Print)},
  doi       = {10.1007/978-3-319-43506-0_46},
  pages     = {525 -- 537},
  year      = {2016},
  language  = {en}
}
@inproceedings{FerreinMeessenLimpertetal.2021,
  author    = {Ferrein, Alexander and Meeßen, Marcus and Limpert, Nicolas and Schiffer, Stefan},
  title     = {Compiling ROS Schooling Curricula via Contentual Taxonomies},
  series = {Robotics in Education},
  booktitle = {Robotics in Education},
  editor    = {Lepuschitz, Wilfried},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-67411-3},
  doi       = {10.1007/978-3-030-67411-3_5},
  pages     = {49 -- 60},
  year      = {2021},
  language  = {en}
}
@inproceedings{SteinbauerFerrein2019,
  author    = {Steinbauer, Gerald and Ferrein, Alexander},
  title     = {CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018.},
  series = {CEUR workshop proceedings},
  booktitle = {CEUR workshop proceedings},
  number    = {Vol-2325},
  issn      = {1613-0073},
  pages     = {46 Seiten},
  year      = {2019},
  language  = {en}
}
@inproceedings{DeyElsenFerreinetal.2021,
  author    = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan},
  title     = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom},
  series = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  booktitle = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  editor    = {Makedon, Fillia},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {9781450387927},
  doi       = {10.1145/3453892.3462697},
  pages     = {292 -- 299},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{ClaerFerreinSchiffer2019,
  author    = {Claer, Mario and Ferrein, Alexander and Schiffer, Stefan},
  title     = {Calibration of a Rotating or Revolving Platform with a LiDAR Sensor},
  series = {Applied Sciences},
  volume    = {Volume 9},
  journal   = {Applied Sciences},
  number    = {issue 11, 2238},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-3417},
  doi       = {10.3390/app9112238},
  pages     = {18 Seiten},
  year      = {2019},
  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}
}
@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}
}
@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}
}
@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{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}
}