  This paper addresses the localization problem. The extended Kalman filter
(EKF) is employed to localize a unicycle-like mobile robot equipped with a
laser range finder (LRF) sensor and an omni-directional camera. The LRF is used
to scan the environment which is described with line segments. The segments are
extracted by a modified least square quadratic method in which a dynamic
threshold is injected. The camera is employed to determine the robot's
orientation. The prediction step of the EKF is performed by extracting
parameters from the kinematic model and input signal of the robot. The
correction step is conducted with the implementation of a line matching
algorithm and the comparison between line's parameters of the local and global
maps. In the line matching algorithm, a conversion matrix is introduced to
reduce the computation cost. Experiments have been carried out in a real mobile
robot system and the results prove the applicability of the method for the
purpose of localization.

Robotics;Systems and Control