The potential of digital-twin technology, involving the creation of precise digital replicas of physical objects, to reshape AR experiences in 3D object tracking and localization scenarios is significant. However, enabling robust 3D object tracking in dynamic mobile AR environments remains a formidable challenge. These scenarios often require a more robust pose estimator capable of handling the inherent sensor-level measurement noise. In this paper, recognizing the challenges of comprehensive solutions in existing literature, we propose a transformer-based 6DoF pose estimator designed to achieve state-of-the-art accuracy under real-world noisy data. To systematically validate the new solution's performance against the prior art, we also introduce a novel RGBD dataset called Digital Twin Tracking Dataset (DTTD) v2, which is focused on digital-twin object tracking scenarios. Expanded from an existing DTTD v1, the new dataset adds digital-twin data captured using a cutting-edge mobile RGBD sensor suite on Apple iPhone 14 Pro, expanding the applicability of our approach to iPhone sensor data. Through extensive experimentation and in-depth analysis, we illustrate the effectiveness of our methods under significant depth data errors, surpassing the performance of existing baselines. Code is made publicly available at: https://github.com/augcog/Robust-Digital-Twin-Tracking.

翻译：数字孪生技术通过创建物理对象的精确数字副本，在三维物体追踪与定位场景中重塑增强现实体验方面潜力巨大。然而，在动态移动增强现实环境中实现稳健的三维物体追踪仍是一项严峻挑战。此类场景通常需要更鲁棒的位姿估计器，以处理固有的传感器级测量噪声。本文针对现有文献中综合解决方案的不足，提出了一种基于Transformer的六自由度位姿估计器，旨在真实噪声数据下达到最先进精度。为系统验证新方案相较于先前技术的性能，我们同时引入了一个名为数字孪生追踪数据集（DTTD）v2的新型RGBD数据集，专注于数字孪生物体追踪场景。该数据集基于现有DTTD v1扩展，新增了使用Apple iPhone 14 Pro前沿移动RGBD传感器套件采集的数字孪生数据，从而将我们方法的适用性扩展至iPhone传感器数据。通过大量实验与深入分析，我们证明了方法在显著深度数据误差下仍能超越现有基线性能。代码已开源：https://github.com/augcog/Robust-Digital-Twin-Tracking