Monocular vision-based target motion estimation is a fundamental challenge in numerous applications. This work introduces a novel bearing-box approach that fully leverages modern 3D detection measurements that are widely available nowadays but have not been well explored for motion estimation so far. Unlike existing methods that rely on restrictive assumptions such as isotropic target shape and lateral motion, our bearing-box estimator can estimate both the target's motion and its physical size without these assumptions by exploiting the information buried in a 3D bounding box. When applied to multi-rotor micro aerial vehicles (MAVs), the estimator yields an interesting advantage: it further removes the need for higher-order motion assumptions by exploiting the unique coupling between MAV's acceleration and thrust. This is particularly significant, as higher-order motion assumptions are widely believed to be necessary in state-of-the-art bearing-based estimators. We support our claims with rigorous observability analyses and extensive experimental validation, demonstrating the estimator's superior performance in real-world scenarios.

翻译：基于单目视觉的目标运动估计是众多应用中的基础性挑战。本研究提出了一种新颖的方位-包围盒方法，该方法充分利用了当前广泛可用但尚未在运动估计中得到深入探索的现代三维检测测量。与现有依赖各向同性目标形状和横向运动等限制性假设的方法不同，我们的方位-包围盒估计器能够通过利用三维边界框中蕴含的信息，在无需这些假设的情况下同时估计目标的运动及其物理尺寸。当应用于多旋翼微飞行器时，该估计器产生了一个有趣的优势：通过利用微飞行器加速度与推力之间的独特耦合关系，进一步消除了对高阶运动假设的需求。这一点尤为重要，因为在高性能的基于方位的估计器中，高阶运动假设被广泛认为是必要的。我们通过严格的可观测性分析和广泛的实验验证支持了我们的主张，证明了该估计器在真实场景中的优越性能。