导航和混合现实应用中AR头显从本地坐标系到大地坐标系的快速对准

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Letters Pub Date : 2025-01-07 DOI:10.1109/LSENS.2025.3526597
Eudald Sangenis;Andrei M. Shkel
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引用次数: 0

摘要

将虚拟现实(VR)和增强现实(AR)技术集成到基于位置的服务和应用程序中,需要在绝对大地测量参考框架内利用纬度、经度和海拔(LLA)坐标进行精确导航。通常情况下,VR/AR头显会建立一个具有任意初始原点和方向的局部笛卡尔(XYZ)世界坐标(WC)框架。为了精确的大地测量导航,将这些设备对准真北(TN)是必不可少的。这封信介绍了一种基于ar的方法,用于实现WC框架相对于大地坐标系的初始对准。我们开发了一个AR用户界面,通过LLA坐标直观地引导用户到已知目标,间接地将系统与TN对齐。使用Magic Leap 2 AR头显,我们将我们的方法与传统的基于磁力计的方法进行了评估。实验结果表明,与传统的磁强计方法相比,我们的方法将平均角误差降低了4倍,标准差($\sigma$)降低了5倍。这种改进可以消除初始磁力计校准的需要,为AR/VR应用中的TN校准提供更高效和强大的解决方案。
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Fast-Alignment of AR Headset From Local to Geodetic Coordinate Frame for Navigation and Mixed Reality Applications
The integration of virtual reality (VR) and augmented reality (AR) technologies into location-based services and applications necessitates precise navigation within an absolute geodetic reference frame, utilizing latitude, longitude, and altitude (LLA) coordinates. Typically, VR/AR headsets establish a local Cartesian (XYZ) world coordinate (WC) frame with an arbitrary initial origin and orientation. For accurate geodetic navigation, it is essential to align these devices to the true North (TN). This letter introduces an AR-based method for achieving the initial alignment of the WC frame relative to the geodetic frame. We developed an AR user interface to visually guide users to a known target with LLA coordinates, indirectly aligning the system to TN. Using a Magic Leap 2 AR headset, we evaluated our approach against traditional magnetometer-based methods. Our experimental results demonstrated that our method reduces the mean angular error by a factor of 4× and the standard deviation ($\sigma$) by 5× compared to traditional magnetometer methods. This improvement can eliminate the need for initial magnetometer calibration, offering a more efficient and robust solution for TN alignment in AR/VR applications.
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
期刊最新文献
Table of Contents Front Cover IEEE Sensors Council Information IEEE Sensors Letters Subject Categories for Article Numbering Information IEEE Sensors Letters Publication Information
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