Dr. András Majdik
Ph.D. University of Cluj-Napoca
Email: majdik (at) ifi (dot) uzh (dot) ch
Office: Andreasstrasse 15, AND 2.20
Phone: +41 44 635 68 23
I received a PhD in 2011 form the Technical University of Cluj-Napoca (TU Cluj) in the field of Simultaneous Localization and Mapping of mobile robots. My PhD thesis was co-advised by Prof. Dr. Gheorghe Lazea from Robotics Research Group, TU Cluj and Prof. Dr. José A. Castellanos from Robotics, Perception and Real Time Group, University of Zaragoza. I graduated in 2008 from the Faculty of Automation & Computer Science, TU Cluj, Romania with an MSc in engineering.
I worked as a visiting researcher: from March to November 2010 at the Department of Computer Science and Systems Engineering, University of Zaragoza, Spain; from January to April 2011 and from July to September 2012 at the 3-D Vision and Mobile Robotics Research Group, Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Hungary.
Volunteering: I regularly review papers for the following conferences: IROS, ICRA, AQTR; I was a local organizing committee member at RSS�10 and AQTR�12.
Please contact me for an up to date CV.
Visual SLAM, Autonomous micro air vehicles, Urban scene recognition
MAV urban localization from Google Street View data
We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists.
To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system.
To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences.
The proposed approach is tested on a 2km image dataset captured with a small quadroctopter flying in the streets of Zurich.
The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.
Adaptive appearance based loop-closing in heterogeneous environments
Budapest City Center data set
In case you would like to get a copy of the data set, please don�t hesitate to contact me.
Robotics and Perception Group
Department of Informatics
University of Zurich
Dr. András Majdik
Room: AND 2.20
Office: +4144 635 6823