Publisher : The Korean Society for Aeronautical & Space Sciences
DOI : 10.5139/IJASS.2010.11.1.031
Title & Authors
Motion and Structure Estimation Using Fusion of Inertial and Vision Data for Helmet Tracker Heo, Se-Jong; Shin, Ok-Shik; Park, Chan-Gook;
For weapon cueing and Head-Mounted Display (HMD), it is essential to continuously estimate the motion of the helmet. The problem of estimating and predicting the position and orientation of the helmet is approached by fusing measurements from inertial sensors and stereo vision system. The sensor fusion approach in this paper is based on nonlinear filtering, especially expended Kalman filter(EKF). To reduce the computation time and improve the performance in vision processing, we separate the structure estimation and motion estimation. The structure estimation tracks the features which are the part of helmet model structure in the scene and the motion estimation filter estimates the position and orientation of the helmet. This algorithm is tested with using synthetic and real data. And the results show that the result of sensor fusion is successful.
Sensor Fusion;Motion and Structure Estimation;Helmet Tracker;
E. Foxlin, Y. Altshuler, L. Naimark and M. Harington, “FlightTracker: A Novel Optical/ Inertial Tracker for Cockpit Enhance Vision”, IEEE/ACM International Sysmposium on Mixed and Augmented Reality November 2-5, 2004, Washington.D.C.
Y.I. Kim, Y.J. Lee and C.G. Park, “Hybrid Head Tracker System to Compose Optical /Inertialg Head Tracker System”, ICSIIT 2007, Bali, Indonesia, July 26 -27, 2007.
Lin Chai, William A. Hoff, Tyrone Vincent, “Three-dimensional motion and structure estimation using inertial sensors and computer vision for augmented reality”, Presence: Teleoperators and Virtual Environments, v.11 n.5, p. 474-492, October 2002.
Richard Hartley, Andrew Zisserman, “Multiple View Geometry in Computer Vision” (2nd edition), Cambridge University Press, 2004.
Peter Gemeiner, Peter Einramhof and Markus Vincze, Zienkiewicz, “Simultaneous Motion and Structure Estimation by Fusion of Inertial and Vision Data”, The international Journal of Robotics Research, Vol. 26, No. 6, June 2007, pp. 591-605.
S.G. Chroust and M. Vincze, “Fusion of Vision and Inertial Data for Motion and Structure Estimation” , The Journal of Robotic Systems, Vol. 21, No. 2, 2004, pp. .
A. Ude, “Filtering in a Unit Quaternion Space for Model-Based Object Tracking”, The Journal of Robotics and Autonomous Systems, Vol. 28, No. 2, August, 1999, pp. 163-170.
Greg Welch and Gary Bishop, “An Introduction to the Kalman Filter”, SIGGRAPH 2001, Course 8.
Berthold K.P. Horn, “Closed form solution of absolute orientation using unit quaternion”, Journal of Optical Society of America, Vol. 4, April, 1987, pp. 629-642.