标题： 基于相对方位测量的模块化机器人与地标定位 显示英文标题

标题： Modular Robot and Landmark Localisation Using Relative Bearing Measurements

摘要： 本文我们提出了一种模块化非线性最小二乘滤波方法，用于由独立子系统组成的系统。每个子系统的状态和误差协方差估计是独立更新的，即使相对测量同时依赖于多个子系统的状态。我们将协方差交集（CI）算法作为我们解决方案的一部分，以防止子系统相互共享估计时的信息重复计算。基于最小二乘估计的CI算法的另一种推导方式使得这种集成成为可能。我们将所提出的的方法具体应用于机器人-地标定位问题。在这个问题中，相对于移动机器人SE(2)位姿测量的静止地标位置的方位角噪声测量将机器人位姿和地标位置的估计问题耦合在一起。在随机模拟研究中，我们将所提出的模块化方法与单一联合状态滤波器进行基准比较，以阐明它们各自的权衡。在该研究中，我们还包含了所提出方法的变体，这些变体在减少通信和带宽需求的情况下实现了性能的渐进退化。 显示英文摘要

摘要： In this paper we propose a modular nonlinear least squares filtering approach for systems composed of independent subsystems. The state and error covariance estimate of each subsystem is updated independently, even when a relative measurement simultaneously depends on the states of multiple subsystems. We integrate the Covariance Intersection (CI) algorithm as part of our solution in order to prevent double counting of information when subsystems share estimates with each other. An alternative derivation of the CI algorithm based on least squares estimation makes this integration possible. We particularise the proposed approach to the robot-landmark localization problem. In this problem, noisy measurements of the bearing angle to a stationary landmark position measured relative to the SE(2) pose of a moving robot couple the estimation problems for the robot pose and the landmark position. In a randomized simulation study, we benchmark the proposed modular method against a monolithic joint state filter to elucidate their respective trade-offs. In this study we also include variants of the proposed method that achieve a graceful degradation of performance with reduced communication and bandwidth requirements.