Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Superior augmented reality registration by integrating landmark tracking and magnetic tracking
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
CyberCode: designing augmented reality environments with visual tags
DARE '00 Proceedings of DARE 2000 on Designing augmented reality environments
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic registration correction in augmented-reality systems
VRAIS '95 Proceedings of the Virtual Reality Annual International Symposium (VRAIS'95)
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Innovative geometric pose reconstruction for marker-based single camera tracking
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
A performance study for camera pose estimation using visual marker based tracking
Machine Vision and Applications
AR-Room: a rapid prototyping framework for augmented reality applications
Multimedia Tools and Applications
Increasing camera pose estimation accuracy using multiple markers
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
| Hi-index | 0.00 |
Augmented reality (AR) deals with the problem of dynamically and accurately align virtual objects with the real world. Among the used methods, vision-based techniques have advantages for AR applications, their registration can be very accurate, and there is no delay between the motion of real and virtual scenes. However, the downfall of these approaches is their high computational cost and lack of robustness. To address these shortcomings we propose a robust camera pose estimation method based on tracking calibrated fiducials in a known 3D environment, the camera location is dynamically computed by the Orthogonal Iteration Algorithm. Experimental results show the robustness and the effectiveness of our approach in the context of real-time AR tracking.