Dual quaternion based IMU and vision fusion framework for mobile augmented reality

Abstract

Mobile Augmented Reality (MAR) is an emerging field and its nascent applications are finding its ways into the current deployments of cyber physical system. Mobile devices can harness augmented reality technology in any unprepared environment. This introduces a challenge to achieve an accurate and robust registration and tracking of mobile device. For accurate tracking, much research is being carried out to fuse inertial and vision sensor data. The resultant tracking can be further made better by finding means to track coupled translational and rotational motions. This problem is tackled with a neat formalism in terms of dual quaternion. Unit dual quaternion can capture the coupling between translational and rotational motions. In this paper, the requisite machinery is pivoted around Extended Kalman Filter (EKF) and is derived based on dual quaternion. The derived EKF expression is verified through experimentation involving both simulated and realistic data, the latter being obtained from a prototype for MAR. The simulation results show the effectiveness of dual quaternion on position and orientation estimation. This novel fusion framework resulted in more accurate tracking as compared to that of the existing quaternion based algorithm.