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

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.

[1]  Xiande Wu,et al.  Relative Status Determination for Spacecraft Relative Motion Based on Dual Quaternion , 2014 .

[2]  Raúl Rojas,et al.  Kalman filter for vision tracking , 2005 .

[3]  David E. Breen,et al.  Confluence of Computer Vision and Interactive Graphies for Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[4]  E. Pennestrì,et al.  Linear algebra and numerical algorithms using dual numbers , 2007 .

[5]  Mongi A. Abidi,et al.  Pose and motion estimation using dual quaternion-based extended Kalman filtering , 1998, Electronic Imaging.

[6]  Suya You,et al.  Fusion of vision and gyro tracking for robust augmented reality registration , 2001, Proceedings IEEE Virtual Reality 2001.

[7]  Henry Been-Lirn Duh,et al.  Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[8]  Prashanth Swamy,et al.  An Improved Tracking using IMU and Vision Fusion for Mobile Augmented Reality Applications , 2014, ArXiv.

[9]  John Vince,et al.  Quaternions for Computer Graphics , 2011 .

[10]  Ben Kenwright,et al.  A Beginners Guide to Dual-Quaternions: What They Are, How They Work, and How to Use Them for 3D Character Hierarchies , 2012, WSCG 2012.

[11]  Mehdi Mekni,et al.  Augmented Reality : Applications , Challenges and Future Trends , 2014 .

[12]  Panagiotis Tsiotras,et al.  Simultaneous position and attitude control without linear and angular velocity feedback using dual quaternions , 2013, 2013 American Control Conference.

[13]  Angelo M. Sabatini,et al.  Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation , 2013, Sensors.

[14]  Thomas B. Schön,et al.  Sensor Fusion for Augmented Reality , 2006, 2006 9th International Conference on Information Fusion.