Robotics and Perception



Modern robot research focuses on complex robot-human and robot-environment interaction. The ability to perform complex actions is connected to the quality of spatial perception the robot has and this depends not only on good sensors, but mostly on good algorithms that enable these machines to understand data collected by their cameras, lasers, microphones and other sensors they have. Our group works actively on robotics research, computer vision for robotics  and on machine learning solutions to fundamental robotic problems, like sensor fusion, robot localization, space perception, movement and human-machine interaction.





  • "A Multiple Kernel Learning Framework for Detecting Altered Fingerprints". International Conference on Pattern Recognition (ICPR). 2012. [More] 
  • "Visual-inertial Tracking on Android for Augmented Reality Applications". EESMS. 2012. [More] 
  • "A Discriminative Approach for Appearance Based Loop Closing". Internation Conference on Intelligent Robots and Systems (IROS). 2012. [More] 
  • "A transfer learning approach for multi-cue semantic place recognition". Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. 2013. pp. 2122 - 2129. [More] 
  • "Evaluation of non-geometric methods for visual odometry", Robotics and Autonomous Systems, Vol. 62, December, 2014, pp. 1717–1730. [More]