Stereo Vision Based Self-localization of Autonomous Mobile Robots Abdul Bais 1 , Robert Sablatnig 2 , Jason Gu 3 , Yahya M. Khawaja 1 , Muhammad Usman 1 , Ghulam M. Hasan 1 , and Mohammad T. Iqbal 4 1 NWFP University of Engineering and Technology Peshawar, Pakistan {bais, yahya.khawja, usman, gmjally}@nwfpuet.edu.pk 2 Vienna University of Technology Vienna, Austria sab@prip.tuwien.ac.at 3 Dalhousie University Halifax, Canada jason.gu@dal.ca 4 Islamia College Peshawar, Pakistan tariq821@hotmail.com Abstract. This paper presents vision based self-localization of tiny au- tonomous mobile robots in a known but highly dynamic environment. The problem covers tracking the robot position with an initial estimate to global self-localization. The algorithm enables the robot to find its ini- tial position and to verify its location during every movement. The global position of the robot is estimated using trilateration based techniques whenever distinct landmark features are extracted. Distance measure- ments are used as they require fewer landmarks compared to methods using angle measurements. However, the minimum required features for global position estimation are not available throughout the entire state space. Therefore, the robot position is tracked once a global position es- timate is available. Extended Kalman filter is used to fuse information from multiple heterogeneous sensors. Simulation results show that the new method that combines the global position estimation with tracking results in significant performance gain. Keywords: self-localization, stereo vision, autonomous robots, Kalman filter, soccer robots. 1 Introduction In an application where multiple robots are autonomously working on a common global task, knowledge of the position of individual robots turns out to be a basic requirement for successful completion of any global strategy. One of the solutions for the robot position estimation is to start at a known location and track the robot position locally using methods such as odometry or inertial navigation [1]. G. Sommer and R. Klette (Eds.): RobVis 2008, LNCS 4931, pp. 367–380, 2008. c  Springer-Verlag Berlin Heidelberg 2008