紧耦合多传感器混合跟踪算法

在增强现实应用中实现对运动目标的准确跟踪是一个具有挑战性的任务。基于混合跟踪通过对多传感器信息的融合通常比单一传感器跟踪算法更为优越的特性，提出了一种新的紧耦合混合跟踪算法实现视觉与惯性传感器信息的实时融合。该算法基于多频率的测量数据同步，通过强跟踪滤波器引入时变衰减因子自适应调整滤波预测误差协方差，实现对运动目标位置数据的准确估计。通过标示物被遮挡状态下的跟踪实验结果表明，该方法能有效改善基于扩展卡尔曼滤波器的混合跟踪算法对运动目标位置信息预测估计的准确性，提高跟踪快速移动目标的稳定性，适用于大范围移动条件下的增强现实系统。

Tightly-coupled multi-sensor hybrid tracking algorithm

Accurate tracking for augmented reality applications is a challenging task. Multi-sensor hybrid tracking generally provides more stable resalts than single visual tracking. A new tightly-coupled hybrid tracking approach combining vision-based systems with an inertial sensor is presented in this paper. Based on the multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter is used to smooth sensor data and estimate the position and orientation. Through adding a time-varying fading factor to adaptively adjust the prediction error covariance of the filter, this method improves the performance of tracking for fast moving targets. Experimental results with occluded markers show that proposed approach can effectively improve the prediction accuracy of location information to target motion with the hybrid tracking algorithm based on the extended Kalman filter, improve the stability of fast moving target tracking. Our approach is suitable for a large range of mobile conditions.