@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{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}
}
@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}
}
@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}
}
@article{FerreinNiemuellerSteinbauer2010,
  author    = {Ferrein, Alexander and Niem{\"u}ller, Tim and Steinbauer, Gerald},
  title     = {Team Zadeat 2010 : application for participation},
  pages     = {5 Seiten},
  year      = {2010},
  language  = {en}
}