Department of Robotics Engineering, DGIST, Daegu, South Korea.
School of Mechanical, Aerospace and Nuclear Engineering, UNIST, Ulsan, South Korea.
PLoS One. 2021 Dec 28;16(12):e0261933. doi: 10.1371/journal.pone.0261933. eCollection 2021.
Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.
虚拟现实 (VR) 技术在许多生物医学应用中发挥着重要作用。这些 VR 场景增加了需要高精度的人类任务的宝贵经验。不幸的是,商业 VR 控制器在微操作任务中存在较大的定位误差。在这里,我们提出了一个基于 VR 的框架以及一个传感器融合算法,以提高微创手术工具的微定位跟踪性能。据我们所知,这是卡尔曼滤波器在毫米级 VR 环境中的首次应用,使用了 VR 控制器和惯性测量设备之间的位置数据。本研究构建并测试了两种情况:(1)无传感器融合跟踪和(2)带主动传感器融合的位置跟踪。静态和动态实验表明,卡尔曼滤波器可以在小尺度 VR 场景中的微操作中提供更高的精度。