随着增强现实技术的发展,仅包含视觉的增强现实技术已经不能满足人们的需求,而加入触觉信息形成的视-触觉融合的增强现实系统却越来越受到关注。触觉设备是将真实环境和虚拟环境连接起来的纽带。然而,触觉设备在视场中的存在会带来两个...随着增强现实技术的发展,仅包含视觉的增强现实技术已经不能满足人们的需求,而加入触觉信息形成的视-触觉融合的增强现实系统却越来越受到关注。触觉设备是将真实环境和虚拟环境连接起来的纽带。然而,触觉设备在视场中的存在会带来两个问题,一方面触觉设备占据了很大的视觉空间,另一方面触觉设备的定位往往会出现较大的误差,导致了视-触觉增强现实的真实性下降。为了增加使用者的体验感,提出一种高保真视-触觉增强现实环境的搭建方法。首先基于改进的ORB(Oriented FAST and Rotated BRIEF)和KLT(Kanade-Lucas-Tomasi)增强现实跟踪注册算法搭建稳定的视觉增强现实环境。其次提出了一种能校准触笔位置和方向的方法,以提升交互体验。然后提出一种基于力反馈的触觉渲染算法,最后使用改进的全局泊松方程和Criminisi算法对触觉设备进行隐藏和修复,减小因触觉设备存在对用户沉浸感造成的影响。展开更多
视触觉增强现实是将触觉感知加入到增强现实中的一种新技术。不仅可以融合真实场景和虚拟对象,还能实现视觉和触觉的同步感知。基于3D Systems Touch触觉设备提出一种新的视触觉交互算法。首先,基于Marker-SLAM算法搭建增强现实环境,用...视触觉增强现实是将触觉感知加入到增强现实中的一种新技术。不仅可以融合真实场景和虚拟对象,还能实现视觉和触觉的同步感知。基于3D Systems Touch触觉设备提出一种新的视触觉交互算法。首先,基于Marker-SLAM算法搭建增强现实环境,用于实时获得相机在地图中的位姿;其次,为了将触觉信息融入到增强现实环境中,提出基于无跟踪器的触控笔尖端姿态优化算法;最后,分别采集测量点在触觉和世界坐标系中的三维信息,通过确定两个坐标系间的刚性变换,将触觉设备的正向运动模型映射到增强现实空间中。所提出的跟踪注册方法的注册准确率均达到90%以上,与基于跟踪器的方法相比,所提出的姿态优化算法获得的校正位置的平均误差为2.3±0.2 mm。展开更多
Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an...Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network(DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.展开更多
文摘随着增强现实技术的发展,仅包含视觉的增强现实技术已经不能满足人们的需求,而加入触觉信息形成的视-触觉融合的增强现实系统却越来越受到关注。触觉设备是将真实环境和虚拟环境连接起来的纽带。然而,触觉设备在视场中的存在会带来两个问题,一方面触觉设备占据了很大的视觉空间,另一方面触觉设备的定位往往会出现较大的误差,导致了视-触觉增强现实的真实性下降。为了增加使用者的体验感,提出一种高保真视-触觉增强现实环境的搭建方法。首先基于改进的ORB(Oriented FAST and Rotated BRIEF)和KLT(Kanade-Lucas-Tomasi)增强现实跟踪注册算法搭建稳定的视觉增强现实环境。其次提出了一种能校准触笔位置和方向的方法,以提升交互体验。然后提出一种基于力反馈的触觉渲染算法,最后使用改进的全局泊松方程和Criminisi算法对触觉设备进行隐藏和修复,减小因触觉设备存在对用户沉浸感造成的影响。
文摘视触觉增强现实是将触觉感知加入到增强现实中的一种新技术。不仅可以融合真实场景和虚拟对象,还能实现视觉和触觉的同步感知。基于3D Systems Touch触觉设备提出一种新的视触觉交互算法。首先,基于Marker-SLAM算法搭建增强现实环境,用于实时获得相机在地图中的位姿;其次,为了将触觉信息融入到增强现实环境中,提出基于无跟踪器的触控笔尖端姿态优化算法;最后,分别采集测量点在触觉和世界坐标系中的三维信息,通过确定两个坐标系间的刚性变换,将触觉设备的正向运动模型映射到增强现实空间中。所提出的跟踪注册方法的注册准确率均达到90%以上,与基于跟踪器的方法相比,所提出的姿态优化算法获得的校正位置的平均误差为2.3±0.2 mm。
基金supported by the National Natural Science Foundation of China (61771070)。
文摘Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network(DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.
