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 F1 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.

翻译：设备姿态跟踪错误导致的虚拟内容不稳定性，在无标记增强现实（AR）中仍是一大常见问题，尤其是在智能手机和平板电脑上。然而，在检查将承载AR体验的环境时，确定这些不稳定伪影将出现在何处颇具挑战性：我们很少能获取地面真实姿态来测量姿态误差，即便姿态误差可得，传统可视化方法也无法将该数据与实际环境关联，从而限制了其实用性。为解决这些问题，我们提出SiTAR（增强现实情境轨迹分析），这是首个融入姿态跟踪误差估计的AR情境轨迹分析系统。首先，我们开发了首个面向视觉惯性同步定位与建图（VI-SLAM）的基于不确定性的姿态误差估计方法，无需地面真实数据即可获得姿态误差估计；在四个VI-SLAM数据集上的评估中，我们实现了高达96.1%的平均准确率和0.77的平均F1分数。接着，我们展示了在ARCore设备上实现的SiTAR系统，它结合了提供基于不确定性姿态误差估计的后端与生成情境轨迹可视化的前端。最后，我们在真实条件下评估了SiTAR的有效性：通过一项包含15名用户和13个多样化环境的野外研究，测试了三种可视化技术；该研究揭示了环境尺度以及表面属性对用户体验和任务表现的影响。