Self Localization of an Autonomous Robot: Using an EKF to merge Odometry and Vision based Landmarks

Abstract

Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a extended Kalman filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown.