Visual Inertial Odometry (VIO) is an essential component of modern Augmented Reality (AR) applications. However, VIO only tracks the relative pose of the device, leading to drift over time. Absolute pose estimation methods infer the device's absolute pose, but their accuracy depends on the input quality. This paper introduces VIO-APR, a new framework for markerless mobile AR that combines an absolute pose regressor (APR) with a local VIO tracking system. VIO-APR uses VIO to assess the reliability of the APR and the APR to identify and compensate for VIO drift. This feedback loop results in more accurate positioning and more stable AR experiences. To evaluate VIO-APR, we created a dataset that combines camera images with ARKit's VIO system output for six indoor and outdoor scenes of various scales. Over this dataset, VIO-APR improves the median accuracy of popular APR by up to 36\% in position and 29\% in orientation, increases the percentage of frames in the high ($0.25 m, 2^{\circ}$) accuracy level by up to 112\% and reduces the percentage of frames predicted below the low ($5 m, 10^\circ$) accuracy greatly. We implement VIO-APR into a mobile AR application using Unity to demonstrate its capabilities. VIO-APR results in noticeably more accurate localization and a more stable overall experience.

翻译：视觉惯性里程计（VIO）是现代增强现实（AR）应用的核心组件。然而，VIO仅能跟踪设备的相对位姿，导致随时间累积漂移。绝对位姿估计方法可推断设备的绝对位姿，但其精度取决于输入质量。本文提出VIO-APR——一种结合绝对位姿回归器（APR）与局部VIO跟踪系统的无标记移动AR新框架。VIO-APR利用VIO评估APR的可靠性，并借助APR识别与补偿VIO漂移。该反馈机制实现了更精准的定位与更稳定的AR体验。为评估VIO-APR，我们构建了包含六种不同规模室内外场景的相机图像与ARKit VIO系统输出数据集。在该数据集上，VIO-APR将主流APR的中位精度提升高达36%（位置）和29%（方向），将高精度（0.25米，2°）帧率提升至112%，并大幅减少低于低精度（5米，10°）预测帧率。我们通过Unity将VIO-APR部署于移动AR应用以展示其能力，结果表明其可实现显著更精准的定位与更稳定的整体体验。