Mobile Robot Vision Tracking System Using Unscented Kalman Filter

Abstract

This thesis introduces a vision tracking system for mobile robot by using an Unscented Kalman Filter (UKF). The proposed system accurately estimates the position and orientation of the mobile robot by integrating information received from encoders, inertial sensors, and active beacons. These position and orientation estimates are used to rotate the camera towards the target during robot motion. The UKF, used as an efficient sensor fusion algorithm, is an advanced filtering technique which reduces the position and orientation errors of the sensors. The designed system compensates for the slip error by switching between two different UKF models, which are designed for slip and no-slip cases, respectively. The slip detector is used to detect the slip condition by comparing the data from the accelerometer and encoder to select the either UKF model as the output of the system. The experimental results show that proposed system is able to locate robot position with significantly reduced position errors and successful tracking of the target for various environments and robot motion scenarios.