Sensor-based self-localization for wheeled mobile robots

Abstract

A reliable and robust algorithm for localizing a mobile robot in an indoor environment that is relatively consistent with an a priori map is demonstrated. The algorithm uses an extended Kalman filter that combines dead-reckoning, ultrasonic, and infrared sensor data to estimate current position and orientation. Through a thresholding approach, unexpected obstacles can be detected. Experimental results from implementation in a mobile robot, Nomad-200, are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>