Application of an extended Kalman filter to multiple low cost navigation sensors in wheeled mobile robots

The present aim of this research is to design a navigation sensor suite for a newly built mobile robot using low cost multiple sensors. This paper addresses the problem of generating navigational data for a wheeled mobile robot. An extended Kalman filter (EKF) is used to fuse data from multiple low cost sensors. In order to estimate the spatial position of a wheeled robot, a combination of accelerometers, a rate gyroscope and two wheel encoders are used. A fundamental principle of dynamic systems is that, if we can measure all internal system states, we have complete freedom in control system design. The system discussed in this paper has more measurement sensors than system states and therefore the sensors give overlapping, low-grade information affected by noise, bias, drift etc. The dynamics of the robot and sensor system are nonlinear. Therefore an EKF is used to fuse these overlapping low-grade measured sensor data and give the best possible estimate of the mobile robot position. A significant advantage of using multiple sensors is that measurement errors can be identified by comparison of different sensor readings.