engleski

Improvement of an Extended Kalman Filter based Mobile Robot Localization

A mobile robot must track its pose in the environment in order to perform any useful task. Problem of finding and tracking the mobile robot's pose is called localization, and can be global or local. In this paper we address the local localization or mobile robot pose tracking with prerequisites of known starting pose, mobile robot kinematics and world model. Pose tracking is mostly based on odometry, which has the problem of accumulating errors in an unbounded fashion. To overcome this problem sensor fusion is commonly used. Extended Kalman filter with two approaches has been used in our work for this purpose. First approach uses only sonar’ s as additional sensors and the second one uses also the built in mobile robot so-called odometric device. Since occupancy grid maps are used, only sonar range measurement uncertainty has to be considered, unlike feature-based maps where an additional uncertainty regarding the feature/range reading assignment must be considered. Thus the numerical complexity is reduced. Proposed localization implementations are experimentally evaluated on a Pioneer 2DX mobile robot in two different set-ups.

wheeled mobile robots; localization; sensor fusion; Kalman filter; occupancy grid maps

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