EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments

Abstract This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the fourwheeled mobile robot equipped with encoders for the wheels and a laser-rangefinder (LRF) sensor. The LRF is used to scan the environment, which is described with line segments. A prediction step is performed by simulating the kinematic model of the robot. In the input noise covariance matrix of the EKF the standard deviation of each robot-wheel’s angular speed is estimated as being proportional to the wheel’s angular speed. A correction step is performed by minimizing the difference between the matched line segments from the local and global maps. If the overlapping rate between the most similar local and global line segments is below the threshold, the line segments are paired. The line parameters’ covariances, which arise from the LRF’s distance-measurement error, comprise the output noise covariance matrix of the EKF...

• Type: Journal
• Year: 2011
• Where: JIRS
• Authors: Luka Teslic, Igor Skrjanc, Gregor Klancar
• Keywords: JIRS 2011, Kalman Filter, Line Segments, Mobile Robot Localization