Extended Kalman filter sensor fusion and application to mobile robot

The main problem in mobile robot is the error accumulation in its position in continuous navigation. In this study the localization of the mobile robot is done with gyroscope and odometric sensors by using the dead-reckoning method. To estimate precise and correct position the dead-reckoning system should be aided by an external absolute positioning sensor. The continuous error accumulation in dead reckoning should be reseted, the position and direction state variables must be updated with an absolute sensor positioning data. The GPS (Global Positioning System) system, which is an absolute positioning system, is used in complement with the dead reckoning system to estimate precise and correct positioning data. The extended Kalman Filter is used for sensor fusion purposes. The Kalman Filter has the ability to make an optimal estimate of the state variable when the data is immerged in white noise. To implement the algorithm, the mobile robot kinematic model was obtained. The kinematic model of the robot is in nonlinear nature. Thus the model is linearized in order to use with the Kalman Filter algorithm. Finally the data obtain from the two different navigation system is perfectly fused and showed with computer simulations.