Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging
Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging
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DOI：
10.1109/ipsn54338.2022.00041
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发表时间：
2022-05
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影响因子：
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通讯作者：
John Miller;Elahe Soltanaghai;R. Duvall;Jeff Chen;Vikram Bhat;Nuno Pereira;Anthony G. Rowe
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文献类型：
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作者：
John Miller;Elahe Soltanaghai;R. Duvall;Jeff Chen;Vikram Bhat;Nuno Pereira;Anthony G. Rowe
当前的协同增强现实(AR)系统通过交换和比较由特征点组成的地图来在用户之间建立共同的定位坐标系。然而，在动态或要素稀疏的环境中，通过地图共享进行相对定位很难实现。它还要求用户交换地图上相同的区域，如果它们被墙隔开或朝向不同的方向，这可能是不可能的。在这篇文章中，我们介绍了Cappella11就像它的音乐灵感一样，Cappella利用代理之间的协作来放弃对乐器的需求，这是一种用于多用户AR应用的无基础设施的6自由度(6DOF)定位系统，它使用用户之间的运动估计和距离测量来建立准确的相对坐标系。Cappella将视觉惯性里程计(VIO)与超宽带(UWB)测距无线电相结合，以一种特别的方式估计每个设备的相对位置。该系统利用协作粒子过滤公式，对附近用户之间交换的零星消息进行操作。与可视地标共享方法不同，这允许协作AR会话，即使用户不共享相同的视场，或者如果环境过于动态而无法进行可靠的特征匹配。我们证明，不仅可以在没有基础设施或全球坐标的情况下执行协作定位，而且我们的方法为AR组合应用提供了几乎与固定基础设施方法相同的精度。Cappella由一个开源的UWB固件和参考手机应用程序组成，可以使用移动AR实时显示团队成员的位置。我们在各种各样的条件下评估了Cappella在多个建筑上的应用，包括跨越多个楼层的30,000平方英尺的连续区域，并发现它在3D中的几何误差中位数小于1米。
Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter.