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：视觉驱动的合作运动估计是许多多机器人系统（如合作空中目标追踪）中的重要问题。该问题可被建模为纯方位合作运动估计，其中视觉测量被描述为从相机指向目标的方位向量。传统的纯方位合作估计方法主要基于分布式卡尔曼滤波（DKF）框架。本文提出了一种基于分布式递归最小二乘法的全新最优纯方位合作估计算法——时空三角测量，该算法相比DKF为分布式估计器设计提供了更灵活的框架。算法设计充分整合了所有可用信息及特定的三角测量几何约束。数值仿真结果表明，该算法在精度和收敛速度方面均优于当前最先进的DKF算法。我们严格证明了所提算法的指数收敛性。此外，为验证算法在实际挑战性条件下的有效性，我们开发了一套基于视觉的合作空中目标追踪系统——据我们所知，这是目前首个实现完全自主运行的此类系统。