Kalman-filtering-based localization of a mobile robot with a LRF in a simulated 2D environment

This article deals with the modelling, simulation and localization of a mobile robot using a laser range finder (LRF) in a 2D environment. The presented simulator is applied for a localization algorithm based on the extended Kalman filter (EKF) design. The prediction of the robot’s pose is performed by simulating the kinematic model of the robot. The input-noise covariance matrix is derived from the noise variances of the angular velocity measurements of both robot wheels. Correction of the robot pose is performed by matching the line parameters of the local environment map to the line parameters of a global map transformed to the robot’s coordinates.