A rapidly converging recursive method for mobile robot localization

This paper proposes a simple method for estimating the position of a robot from relatively few sensor readings. Our algorithms are intended for applications where sensor readings are expensive or otherwise limited, and the readings that are taken are subject to considerable errors or noise. This method exhibits faster convergence with fewer measurements and greater accuracy than that exhibited by the discrete Kalman filter in this type of application. Our approach is validated with a mobile robot, on which a camera is used to obtain bearing information with respect to landmarks in the environment.