基于自然特征点的实时增强现实注册算法

提出了一种基于自然特征点的实时跟踪注册算法,并将其应用于增强现实系统。算法在已知场景的3D模型以及少量标定关键帧图像的基础上,选择与当前图像最为匹配的关键帧,利用基于关键帧的图像匹配方法实时获取摄像机的运动参数估计。算法采用合成中间图像的技术解决两图像特征点间的宽基线匹配问题,并应用扩展卡尔曼滤波器对参数估计结果进行平滑,以消除系统抖动现象。实验结果表明,算法鲁棒性强,注册精度高,能够有效地克服系统误差漂移。

Stable Online Natural Feature-based Registration Algorithm for AR System

An efficient approach of real-time camera registration applied for augmented reality systems was presented, which purely relied on a single camera for visual tracking of the nature features extracted from the real environment. Giving a small number of calibrated key-frames and a rough 3D model of the part of the real scene, camera pose could be accurately achieved by matching inputting image and the key-frame, whose viewpoint was as close as possible to the current inputting one. Wide baseline correspondence problem was solved by rendering intermediate image. Extended Kalman filter was applied for jitter correction. Algorithm performance was tested by real image sequences. Experimental results demonstrate that the registration algorithm not only is accurate and robust, but also can handle significant aspect changes.