@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}
}
@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{ChajanSchulteTiggesRekeetal.2021,
  author    = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas},
  title     = {GPU based model-predictive path control for self-driving vehicles},
  series = {IEEE Intelligent Vehicles Symposium (IV)},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE},
  isbn      = {978-1-7281-5394-0},
  doi       = {10.1109/IV48863.2021.9575619},
  pages     = {1243 -- 1248},
  year      = {2021},
  abstract  = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.},
  language  = {en}
}
@inproceedings{HofmannLimpertMatareetal.2019,
  author    = {Hofmann, Till and Limpert, Nicolas and Matar{\´e}, Viktor and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning},
  series = {RoboCup 2019: Robot World Cup XXIII. RoboCup},
  booktitle = {RoboCup 2019: Robot World Cup XXIII. RoboCup},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-35699-6},
  doi       = {10.1007/978-3-030-35699-6_41},
  pages     = {504 -- 516},
  year      = {2019},
  language  = {en}
}
@inproceedings{KirschMatareFerreinetal.2020,
  author    = {Kirsch, Maximilian and Matar{\´e}, Victor and Ferrein, Alexander and Schiffer, Stefan},
  title     = {Integrating golog++ and ROS for Practical and Portable High-level Control},
  series = {12th International Conference on Agents and Artificial Intelligence},
  booktitle = {12th International Conference on Agents and Artificial Intelligence},
  doi       = {10.5220/0008984406920699},
  year      = {2020},
  language  = {en}
}
@article{HofmannLimpertMatareetal.2018,
  author    = {Hofmann, Till and Limpert, Nicolas and Matar{\´e}, Victor and Sch{\"o}nitz, Sebastian and Niemueller, Tim and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {The Carologistics RoboCup Logistics Team 2018},
  year      = {2018},
  abstract  = {The Carologistics team participates in the RoboCup Logistics League for the seventh year. The RCLL requires precise vision, manipulation and path planning, as well as complex high-level decision making and multi-robot coordination. We outline our approach with an emphasis on recent modifications to those components. The team members in 2018 are David Bosen, Christoph Gollok, Mostafa Gomaa, Daniel Habering, Till Hofmann, Nicolas Limpert, Sebastian Sch{\"o}nitz, Morian Sonnet, Carsten Stoffels, and Tarik Viehmann. This paper is based on the last year's team description.},
  language  = {en}
}
@inproceedings{RekePeterSchulteTiggesetal.2020,
  author    = {Reke, Michael and Peter, Daniel and Schulte-Tigges, Joschua and Schiffer, Stefan and Ferrein, Alexander and Walter, Thomas and Matheis, Dominik},
  title     = {A Self-Driving Car Architecture in ROS2},
  series = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa},
  booktitle = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa},
  isbn      = {978-1-7281-4162-6},
  doi       = {10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020},
  pages     = {1 -- 6},
  year      = {2020},
  language  = {en}
}
@article{LimpertWiesenFerreinetal.2019,
  author    = {Limpert, Nicolas and Wiesen, Patrick and Ferrein, Alexander and Kallweit, Stephan and Schiffer, Stefan},
  title     = {The ROSIN Project and its Outreach to South Africa},
  series = {R\&D Journal},
  volume    = {35},
  journal   = {R\&D Journal},
  pages     = {1 -- 6},
  year      = {2019},
  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}
}
@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}
}