Improving the accuracy of the autonomous mobile robot localization systems based on the multiple sensor fusion methods

Localization system plays an important role in navigation frameworks of autonomous mobile robots. Because, it provides significant information for the remainder systems of the navigation frameworks. Recently, to improve the accuracy of the robot pose estimation system in dynamic environments, the mobile robots are equipped with a variety of sensors, such as wheel encoders, a global positioning system (GPS) sensor, and an inertial measurement unit (IMU) sensor. In this paper, we propose an improved localization system for autonomous mobile robots using multiple sensor fusion techniques. To accomplish that, an extended Kalman filter (EKF) algorithm is utilized to fuse the data from the wheel encoders, GPS and IMU sensors. The simulation results show that, our proposed localization system is able to provide higher accuracy of estimating mobile robot's pose than conventional systems.