Robust Camera Pose Tracking for Augmented Reality Using Particle Filtering Framework
Abstract
In this paper, we present new solutions for the problem of estimating the camera pose using particle filtering framework. The proposed approach is suitable for real-time augmented reality (AR) applications in which the camera is held by the user. This work demonstrates that particle filtering improve the robustness of the tracking comparing to existing approaches, such as those based on the Kalman filter. We propose a tracking framework for both points and lines features, the particle filter is used to compute the posterior density for the camera 3D motion parameters. We also analyze the sensitivity of our technique when outliers are present in the match data. Outliers arise frequently due to incorrect correspondences which occur because of either image noise or occlusion. Results from real data in an augmented reality setup are then presented, demonstrating the efficiency and robustness of the proposed method.