Dead Reckoning Navigation for Autonomous Mobile Robots

Abstract A dead reckoning navigation system for autonomous mobile robot localization is presented. The navigation system was implemented by novel sensor fusion using a Kalman filter. A differential encoder and the gyroscope error models are developed for the filter. An indirect Kalman filter scheme is adopted to reduce the computational burden and to enhance the navigation system reliability. The filter, which feeds back the estimated errors to the main navigation system, mutually compensates the encoder errors and the gyroscope errors. To evaluate the filter performance, the observability of the filter was analyzed. The characteristics of unbounded position error growth in dead reckoning navigation system' has been shown by the observability analysis. The experimental results show that the proposed mobile robot navigation algorithm provides the reliable position and heading angle of the mobile robot without any help of external positioning system.