@inproceedings{FerreinNiemuellerSchifferetal.2013,
  author    = {Ferrein, Alexander and Niem{\"u}ller, Tim and Schiffer, Stefan and Lakemeyer, Gerhard},
  title     = {Lessons learnt from developing the embodied AI platform CAESAR for domestic service robotics},
  series = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)},
  booktitle = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)},
  editor    = {Boots, Byron},
  organization    = {American Association for Artificial Intelligence},
  isbn      = {9781577356011},
  pages     = {21 -- 26},
  year      = {2013},
  language  = {en}
}
@inproceedings{FerreinKallweitScholletal.2015,
  author    = {Ferrein, Alexander and Kallweit, Stephan and Scholl, Ingrid and Reichert, Walter},
  title     = {Learning to Program Mobile Robots in the ROS Summer School Series},
  series = {Proceedings 6th International Conference on Robotics in Education (RiE 15)},
  booktitle = {Proceedings 6th International Conference on Robotics in Education (RiE 15)},
  pages     = {6 S.},
  year      = {2015},
  abstract  = {The main objective of our ROS Summer School series is to introduce MA level students to program mobile robots with the Robot Operating System (ROS). ROS is a robot middleware that is used my many research institutions world-wide. Therefore, many state-of-the-art algorithms of mobile robotics are available in ROS and can be deployed very easily. As a basic robot platform we deploy a 1/10 RC cart that is wquipped with an Arduino micro-controller to control the servo motors, and an embedded PC that runs ROS. In two weeks, participants get to learn the basics of mobile robotics hands-on. We describe our teaching concepts and our curriculum and report on the learning success of our students.},
  language  = {en}
}
@article{FerreinKonurLakemeyer2004,
  author    = {Ferrein, Alexander and Konur, Savas and Lakemeyer, Gerhard},
  title     = {Learning Decision Trees for Action Selection in Soccer Agents / Konur, Savas ; Ferrein, Alexander ; Lakemeyer, Gerhard},
  pages     = {1 -- 7},
  year      = {2004},
  language  = {en}
}
@article{FerreinStrackLakemeyer2006,
  author    = {Ferrein, Alexander and Strack, Andreas and Lakemeyer, Gerhard},
  title     = {Laser-Based Localization with Sparse Landmarks / Strack, Andreas ; Ferrein, Alexander ; Lakemeyer, Gerhard},
  series = {RoboCup 2005: Robot Soccer World Cup IX},
  journal   = {RoboCup 2005: Robot Soccer World Cup IX},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-540-35437-6},
  pages     = {569 -- 576},
  year      = {2006},
  language  = {en}
}
@article{FerreinBeckLakemeyer2009,
  author    = {Ferrein, Alexander and Beck, Daniel and Lakemeyer, Gerhard},
  title     = {Landmark-Based Representations for Navigating Holonomic Soccer Robots / Beck, Daniel ; Ferrein, Alexander ; Lakemeyer, Gerhard},
  series = {RoboCup 2008 : robot soccer world cup XII / Luca Iocchi ... (eds.)},
  journal   = {RoboCup 2008 : robot soccer world cup XII / Luca Iocchi ... (eds.)},
  publisher = {Springer},
  address   = {Heidelberg},
  isbn      = {978-3-642-02921-9},
  pages     = {25 -- 36},
  year      = {2009},
  language  = {en}
}
@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}
}
@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}
}
@inproceedings{KrueckelNoldenFerreinetal.2015,
  author    = {Kr{\"u}ckel, Kai and Nolden, Florian and Ferrein, Alexander and Scholl, Ingrid},
  title     = {Intuitive visual teleoperation for UGVs using free-look augmented reality displays},
  series = {2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA},
  booktitle = {2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA},
  doi       = {10.1109/ICRA.2015.7139809},
  pages     = {4412 -- 4417},
  year      = {2015},
  language  = {en}
}
@inproceedings{NiemuellerNeumannHenkeetal.2017,
  author    = {Niemueller, Tim and Neumann, Tobias and Henke, Christoph and Sch{\"o}nitz, Sebastian and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard},
  title     = {International Harting Open Source Award 2016: Fawkes for the RoboCup Logistics League},
  series = {RoboCup 2016: RoboCup 2016: Robot World Cup XX. RoboCup 2016},
  booktitle = {RoboCup 2016: 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_53},
  pages     = {634 -- 642},
  year      = {2017},
  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}
}