用无迹变换提升仅靠测距的多机协同定位精度
Analysis of the Unscented Transform for Cooperative Localization with Ranging-Only Information
- 用无迹变换处理测距数据中的非线性与不确定性
- 在未知相关性下实现状态与协方差联合融合
- 适合解决传感器噪声大、信息重复使用的定位场景
多智能体机器人系统中的协同定位面临挑战，尤其当仅依赖相邻个体间的测距信息时。核心难题包括：如何利用有限信息提升位置估计精度；如何处理传感器噪声、非线性及代理间测量的未知相关性；以及避免信息重复使用。本文研究无迹变换（UT）在仅测距条件下的状态估计算法，结合协方差交叉（CI）方法处理未知相关性。与卡尔曼滤波不同，CI方法可融合完整的状态与协方差估计，但难以适配仅测距场景。为此，本文采用UT处理不确定性，基于测距数据与当前协同状态估计构造合作更新。该方法引入了测量更新中的信息重用问题。因此，本文旨在评估该方法在不同状态测量不确定性和误差水平下的有效性与局限性。
原文摘要 · Abstract (English)
Cooperative localization in multi-agent robotic systems is challenging, especially when agents rely on limited information, such as only peer-to-peer range measurements. Two key challenges arise: utilizing this limited information to improve position estimation; handling uncertainties from sensor noise, nonlinearity, and unknown correlations between agents measurements; and avoiding information reuse. This paper examines the use of the Unscented Transform (UT) for state estimation for a case in which range measurement between agents and covariance intersection (CI) is used to handle unknown correlations. Unlike Kalman Filter approaches, CI methods fuse complete state and covariance estimates. This makes formulating a CI approach with ranging-only measurements a challenge. To overcome this, UT is used to handle uncertainties and formulate a cooperative state update using range measurements and current cooperative state estimates. This introduces information reuse in the measurement update. Therefore, this work aims to evaluate the limitations and utility of this formulation when faced with various levels of state measurement uncertainty and errors.
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