Localization through fusion of discrete and continuous epipolar geometry with wheel and IMU odometry

Abstract

This paper describes a novel sensor fusion implementation to improve the accuracy of robot localization by combining multiple visual odometry approaches with wheel and IMU odometry. Discrete and continuous Homography Matrices are used to recover position, orientation, and velocity from image sequences of tracked feature points. An Inertial Measurement Unit (IMU) and wheel encoders also measure linear and angular velocity of mobile robot. A Kalman filter fuses the measurements from the visual and inertial measurement systems. Time varying matrices in the Kalman filter allow each sensor to receive higher or lower weight in situations where each is more or less accurate. Experiments are performed with a camera and a IMU (Wiimote controller) mounted on a mobile robot.