SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation
SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation
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DOI：
10.1109/ismar59233.2023.00043
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发表时间：
2023-08
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影响因子：
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通讯作者：
T. Scargill;Ying Chen;Tian-ran Hu;M. Gorlatova
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文献类型：
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作者：
T. Scargill;Ying Chen;Tian-ran Hu;M. Gorlatova
由设备姿态跟踪误差引起的虚拟内容不稳定性仍然是无标记增强现实（AR）中的普遍问题，特别是在智能手机和平板电脑上。然而，当检查将承载AR体验的环境时，确定这些不稳定伪影将发生在哪里是具有挑战性的;我们很少能够访问地面实况姿势来测量姿势误差，即使姿势误差可用，传统的可视化也不会将该数据与真实的环境联系起来，从而限制了它们的有用性。为了解决这些问题，我们提出了SiTAR（增强现实的定位轨迹分析），第一个定位轨迹分析系统的AR，结合姿态跟踪误差的估计。首先，我们开发了第一个基于不确定性的姿态误差估计方法，用于视觉-惯性同时定位和映射（VI-SLAM），这使我们能够在没有地面实况的情况下获得姿态误差估计;我们在四个VI-SLAM数据集上的评估中实现了高达96.1%的平均准确度和高达0.77的平均FI分数。接下来，我们介绍了我们的SiTAR系统，该系统为ARCore设备实现，将提供基于不确定性的姿势误差估计的后端与生成位置轨迹可视化的前端相结合。最后，我们评估了SiTAR在现实条件下的有效性，通过测试三种可视化技术，在野外研究与15个用户和13个不同的环境，这项研究揭示了环境规模和表面的属性，目前可以对用户体验和任务性能的影响。
Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average FI score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next, we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.