In this article, we demonstrate a reliable, robust, and computationally efficient algorithm that uses inexpensive hardware to localize a mobile robot in a rather structured environment that is relatively consistent to an a priori map. Furthermore, the incorporation of thresholding makes possible the localization of the robot even in the presence of objects not depicted in the a priori map. An Extended Kalman Filter is used to combine dead‐reckoning, ultrasonic, and infrared sensor data to estimate current position and orientation. Implementation issues and experimental results from experience with a mobile robot, Nomad 200, are also presented. © 1995 John Wiley & Sons, Inc.
ASJC Scopus subject areas
- Control and Systems Engineering