@inproceedings{KallweitFerreinScholletal.2014,
  author    = {Kallweit, Stephan and Ferrein, Alexander and Scholl, Ingrid and Reichert, Walter},
  title     = {Mobile Autonome Systeme in der Lehre, am Beispiel der ROS Summer School},
  series = {Tagungsband AALE 2014 : das Forum f{\"u}r Fachleute der Automatisierungstechnik aus Hochschulen und Wirtschaft ; 11. Fachkonferenz, Regensburg ; [11. Konferenz f{\"u}r Angewandte Automatisierungstechnik in Lehre und Entwicklung / Ostbayerische Technische Hochschule Regensburg; VFAALE, Verein der Freunde und F{\"o}rderer der Angewandten Automatisierungstechnik]},
  booktitle = {Tagungsband AALE 2014 : das Forum f{\"u}r Fachleute der Automatisierungstechnik aus Hochschulen und Wirtschaft ; 11. Fachkonferenz, Regensburg ; [11. Konferenz f{\"u}r Angewandte Automatisierungstechnik in Lehre und Entwicklung / Ostbayerische Technische Hochschule Regensburg; VFAALE, Verein der Freunde und F{\"o}rderer der Angewandten Automatisierungstechnik]},
  publisher = {Dt. Industrieverl.},
  address   = {M{\"u}nchen},
  isbn      = {978-3-8356-7142-3},
  pages     = {157 -- 163},
  year      = {2014},
  language  = {de}
}
@article{AlhwarinFerreinScholl2014,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {IR stereo kinect: improving depth images by combining structured light with IR stereo},
  pages     = {1 -- 9},
  year      = {2014},
  language  = {en}
}
@inproceedings{NeumannFerreinKallweitetal.2014,
  author    = {Neumann, Tobias and Ferrein, Alexander and Kallweit, Stephan and Scholl, Ingrid},
  title     = {Towards a mobile mapping robot for underground mines},
  series = {7th Conference of Robotics and Mechatronics : RobMech 2014 : 27th and 28th Nov. 2014, Cape Town},
  booktitle = {7th Conference of Robotics and Mechatronics : RobMech 2014 : 27th and 28th Nov. 2014, Cape Town},
  organization    = {Conference of Robotics and Mechatronics <7, 2014, Cape Town, South Africa>},
  pages     = {1 -- 6},
  year      = {2014},
  language  = {en}
}
@incollection{AlhwarinFerreinScholl2014,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {IR stereo kinect: improving depth images by combining structured light with IR stereo},
  series = {PRICAI 2014: Trends in artificial intelligence : 13th Pacific Rim International Conference on Artificial Intelligence : Gold Coast, QLD, Australia, December 1-5, 2014 : proceedings. (Lecture notes in computer science ; vol. 8862)},
  booktitle = {PRICAI 2014: Trends in artificial intelligence : 13th Pacific Rim International Conference on Artificial Intelligence : Gold Coast, QLD, Australia, December 1-5, 2014 : proceedings. (Lecture notes in computer science ; vol. 8862)},
  publisher = {Springer},
  address   = {M{\"u}nchen},
  isbn      = {978-3-319-13559-5 (Print) ; 978-3-319-13560-1 (E-Book)},
  doi       = {10.1007/978-3-319-13560-1_33},
  pages     = {409 -- 421},
  year      = {2014},
  abstract  = {RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.},
  language  = {en}
}