Position estimation for a mobile robot using data fusion

Abstract

This paper describes a position estimation technique based on the fusion of data obtained by two independent subsystems in a mobile robot navigation context. The first subsystem is a self-location one composed of an onboard camera, an onboard image processing unit and artificial landmarks; the second one is a dead-reckoning subsystem based on odometry. The robot navigation system integrates the position estimation obtained by the vision subsystem with the position estimated by odometry using a Kalman filter framework.