Mobile AR applications benefit from fast initialization to display world-locked effects instantly. However, standard visual odometry or SLAM algorithms require motion parallax to initialize (see Figure 1) and, therefore, suffer from delayed initialization. In this paper, we present a 6-DoF monocular visual odometry that initializes instantly and without motion parallax. Our main contribution is a pose estimator that decouples estimating the 5-DoF relative rotation and translation direction from the 1-DoF translation magnitude. While scale is not observable in a monocular vision-only setting, it is still paramount to estimate a consistent scale over the whole trajectory (even if not physically accurate) to avoid AR effects moving erroneously along depth. In our approach, we leverage the fact that depth errors are not perceivable to the user during rotation-only motion. However, as the user starts translating the device, depth becomes perceivable and so does the capability to estimate consistent scale. Our proposed algorithm naturally transitions between these two modes. We perform extensive validations of our contributions with both a publicly available dataset and synthetic data. We show that the proposed pose estimator outperforms the classical approaches for 6-DoF pose estimation used in the literature in low-parallax configurations. We release a dataset for the relative pose problem using real data to facilitate the comparison with future solutions for the relative pose problem. Our solution is either used as a full odometry or as a preSLAM component of any supported SLAM system (ARKit, ARCore) in world-locked AR effects on platforms such as Instagram and Facebook.

中文翻译：

---

移动 AR 的即时视觉里程计初始化

移动 AR 应用程序受益于快速初始化以立即显示世界锁定效果。然而，标准的视觉里程计或 SLAM 算法需要运动视差来初始化（见图 1），因此会受到延迟初始化的影响。在本文中，我们提出了一种 6-DoF 单目视觉里程计，它可以立即初始化并且没有运动视差。我们的主要贡献是一个姿态估计器，它将 5-DoF 相对旋转和平移方向的估计与 1-DoF 平移幅度解耦。虽然在单目视觉设置中无法观察到比例，但在整个轨迹上估计一致的比例（即使物理上不准确）仍然是最重要的，以避免 AR 效果沿深度错误移动。在我们的方法中，我们利用这样一个事实，即在仅旋转运动期间用户无法感知深度误差。然而，当用户开始平移设备时，深度变得可感知，估计一致比例的能力也是如此。我们提出的算法自然地在这两种模式之间转换。我们使用公开可用的数据集和合成数据对我们的贡献进行了广泛的验证。我们表明，所提出的姿态估计器优于文献中在低视差配置中使用的 6-DoF 姿态估计的经典方法。我们使用真实数据发布了相对姿势问题的数据集，以方便与相对姿势问题的未来解决方案进行比较。我们的解决方案要么用作完整的里程计，要么用作任何支持的 SLAM 系统（ARKit、