@book{Scholl1996,
  author    = {Scholl, Ingrid},
  title     = {Bildverarbeitung f{\"u}r die Medizin : Algorithmen - Systeme - Anwendungen ; proceedings des Aachener Workshops am 8. und 9. November 1996 / Institut f{\"u}r Medizinische Informatik und Biometrie der RWTH Aachen. Hrsg. von Thomas Lehmann ; Ingrid Scholl ; Klaus Spitzer},
  editor    = {Lehmann, Thomas Martin and Spitzer, Klaus},
  publisher = {Verlag der Augustinus-Buchhandlung},
  address   = {Aachen},
  isbn      = {3-86073-519-5},
  pages     = {XIII, 427 S. : Ill., graph. Darst.},
  year      = {1996},
  language  = {de}
}
@incollection{FerreinNikolovskiLimpertetal.2023,
  author    = {Ferrein, Alexander and Nikolovski, Gjorgji and Limpert, Nicolas and Reke, Michael and Schiffer, Stefan and Scholl, Ingrid},
  title     = {Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation},
  series = {Multi-Robot Systems - New Advances},
  booktitle = {Multi-Robot Systems - New Advances},
  editor    = {K{\"u}{\c{c}}{\"u}k, Serdar},
  publisher = {Intech Open},
  address   = {London},
  isbn      = {978-1-83768-290-4},
  doi       = {10.5772/intechopen.113044},
  pages     = {21 Seiten},
  year      = {2023},
  abstract  = {In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map.},
  language  = {de}
}