This paper is dedicated to achieving scalable relative state estimation using inter-robot Euclidean distance measurements. We consider equipping robots with distance sensors and focus on the optimization problem underlying relative state estimation in this setup. We reveal the commonality between this problem and the coordinates realization problem of a sensor network. Based on this insight, we propose an effective unconstrained optimization model to infer the relative states among robots. To work on this model in a distributed manner, we propose an efficient and scalable optimization algorithm with the classical block coordinate descent method as its backbone. This algorithm exactly solves each block update subproblem with a closed-form solution while ensuring convergence. Our results pave the way for distance measurements-based relative state estimation in large-scale multi-robot systems.
翻译:本文致力于使用机器人间欧几里得距离测量实现可扩展的相对状态估计。我们考虑配备距离传感器的机器人,并关注相对状态估计背后的优化问题。我们揭示了这个问题与传感器网络的坐标实现问题之间的共性。基于这一观察结果,我们提出了一种有效的无约束优化模型来推断机器人之间的相对状态。为了以分布式方式处理这个模型,我们提出了一种高效且可扩展的优化算法,其骨干采用经典的块坐标下降法。该算法使用闭式解精确求解每个块更新子问题,并确保收敛性。我们的结果为大型多机器人系统中基于距离测量的相对状态估计铺平了道路。