Self-localization of the mobile robot utilizing the heterogeneous sensor information fusion
运用异质传感器信息融合的移动机器人自定位

It is difficult to realize the exact self-localization of mobile robot by using a single type sensor. The heterogeneous sensor information fusion is utilized to improve the self-localization precision. First, the motion model of the mobile robot and observed model of CCD vidicon are established. The optimal state estimation is derived, model disturbances and measurement noises are restrained by the Q;R matrices, and the self-localization is realized by the extended Kalman filter. Then, the observed model of the ultrasonic sensor is established, and the self-localization information is obtained. Finally, the data from CCD vidicon and the ultrasonic sensor are fused by BP neural network. The cooperation of the two types of sensors is realized. The simulation results show that the self-localization precision of the mobile robot is obviously improved by the heterogeneous sensor information fusion.