SENSOR-BASED SELF-LOCALIZATION FOR WHEELED MOBILE ROBOTS

In this article, we demonstrate a reliable, robust, and computationall y efficient algorithm that uses inexpensive hardware to localize a mob ile robot in a rather structured environment that is relatively consis tent to an a priori map. Furthermore, the incorporation of thresholdin g makes possible the localization of the robot even in the presence of objects not depicted in the a priori map. An Extended Kalman Filter i s used to combine dead-reckoning, ultrasonic, and infrared sensor data to estimate current position and orientation. Implementation issues a nd experimental results from experience with a mobile robot, Nomad 200 , are also presented. (C) 1995 John Wiley & Sons, Inc.