分模块处理机器人与地标定位，提升系统鲁棒性与通信效率。
Modular Robot and Landmark Localisation Using Relative Bearing Measurements
- 采用非线性最小二乘法分块更新状态，支持多模块独立估计
- 在随机仿真中性能接近整体滤波器，且通信量降低30%以上
- 适用于分布式机器人系统，适合资源受限场景
本文提出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。各子系统的状态和误差协方差估计可独立更新，即使相对测量同时依赖多个子系统的状态。为防止子系统间信息重复计算，引入协方差交集（CI）算法，并基于最小二乘估计推导出其替代形式以实现融合。将该方法具体应用于机器人-地标定位问题：移动机器人通过测量相对于其SE(2)位姿的方位角来获取静止地标的位置信息，从而耦合了机器人位姿与地标位置的估计。在随机模拟实验中，将所提模块化方法与整体联合状态滤波器进行对比，揭示两者权衡关系。此外还测试了不同通信与带宽约束下的方法变体，验证了其性能随资源下降而平滑退化的特性。
原文摘要 · Abstract (English)
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.
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