Information Engineering Department, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy.
Sensors (Basel). 2020 Mar 6;20(5):1444. doi: 10.3390/s20051444.
The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions.
计算能力和移动图形能力的提高使得具有高效头戴式跟踪解决方案的独立增强现实 (AR) 耳机成为可能。基于成熟的红外标记跟踪的自我运动估计确保了足够的准确性和鲁棒性。不幸的是，基线短且在不受控制的照明条件下运行的可穿戴可见光立体相机由于跟踪失败和姿势估计中的歧义而受到影响。为了提高光学自我跟踪的准确性及其对标记遮挡、降级相机校准和不一致照明的弹性，在这项工作中，我们提出了一种基于卡尔曼滤波的传感器融合方法，该方法在计算运动相关性时将光学跟踪数据与惯性跟踪数据集成在一起。为了衡量 AR 覆盖精度的提高，我们使用专门设计的 AR 耳机进行了实验，该耳机用于支持在直接视觉下执行的复杂手动任务。实验结果表明，所提出的解决方案将头戴式显示器 (HMD) 的跟踪精度提高了三分之一，并通过在一些标记被遮挡并且由于在不可控照明条件下使用头戴式立体跟踪相机的限制导致光学跟踪产生不稳定和/或模糊结果时也捕获目标场景的方向，提高了鲁棒性。
