A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Computer Vision, Graphics, and Image Processing
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact and Approximate Solutions of the Perspective-Three-Point Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Presence: Teleoperators and Virtual Environments
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Robust camera pose estimation using 2d fiducials tracking for real-time augmented reality systems
VRCAI '04 Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry
Fast algorithm for robust template matching with M-estimators
IEEE Transactions on Signal Processing
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Vision-based tracking systems are widely used for augmented reality (AR) applications. Their registration can be very accurate and there is no delay between real and virtual scene. However, vision-based tracking often suffers from limited range, errors, heavy processing time and present erroneous behavior due to numerical instability. To address these shortcomings, robust method are required to overcome these problems. In this paper, we survey classic vision-based pose computations and present a method that offers increased robustness and accuracy in the context of real-time AR tracking. In this work, we aim to determine the performance of four pose estimation methods in term of errors and execution time. We developed a hybrid approach that mixes an iterative method based on the extended Kalman filter (EKF) and an analytical method with direct resolution of pose parameters computation. The direct method initializes the pose parameters of the EKF algorithm which performs an optimization of these parameters thereafter. An evaluation of the pose estimation methods was obtained using a series of tests and an experimental protocol. The analysis of results shows that our hybrid algorithm improves stability, convergence and accuracy of the pose parameters.