Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based on the framework distributed Kalman filtering (DKF). In this paper, we propose a new optimal bearing-only cooperative estimation algorithm, named spatial-temporal triangulation, based on the method of distributed recursive least squares, which provides a more flexible framework for designing distributed estimators than DKF. The design of the algorithm fully incorporates all the available information and the specific triangulation geometric constraint. As a result, the algorithm has superior estimation performance than the state-of-the-art DKF algorithms in terms of both accuracy and convergence speed as verified by numerical simulation. We rigorously prove the exponential convergence of the proposed algorithm. Moreover, to verify the effectiveness of the proposed algorithm under practical challenging conditions, we develop a vision-based cooperative aerial target pursuit system, which is the first of such fully autonomous systems so far to the best of our knowledge.

翻译：视觉协同运动估计是许多多机器人系统（如协同空中目标追踪）中的重要问题。该问题可建模为仅用方位角量的协同运动估计，其中视觉测量被建模为从相机指向目标的方位矢量。传统仅用方位角量的协同估计方法主要基于分布式卡尔曼滤波（DKF）框架。本文基于分布式递推最小二乘法提出了一种新的最优仅用方位角量协同估计算法——时空三角化，该方法为设计分布式估计器提供了比DKF更灵活的框架。该算法设计充分融入了所有可用信息及特定的三角化几何约束。数值仿真验证表明，与现有最先进的DKF算法相比，该算法在精度和收敛速度方面均具有更优的估计性能。我们严格证明了所提算法的指数收敛性。此外，为验证所提算法在实际复杂条件下的有效性，我们开发了视觉协同空中目标追踪系统——据我们所知，这是目前首个全自主的此类系统。