用机器人集群估算位置相关的GPS误差，提升定位精度。
Estimating Spatially-Dependent GPS Errors Using a Swarm of Robots
- 通过机器人间测距测向估计环境中的GPS偏差变化。
- 结合高斯过程回归建模，误差估计精度达90%以上。
- 适合需要高精度定位的无人机、自动驾驶场景。
外部因素（如城市峡谷、恶意干扰）会导致全球定位系统（GPS）误差随位置变化。本文针对静态空间相关误差函数的估计问题，提出一种状态偏置估计算法（SBE），利用机器人团队间的距离与方位感知数据，估算环境中偏置的变化。多架无人机在二维环境中移动，分别采集GPS、测距和测向数据。SBE在估计的位置上计算出的偏差用于训练高斯过程回归（GPR）模型。同时采用基于稀疏高斯过程的信息路径规划（IPP）算法，识别环境中有价值的数据采集区域。每轮迭代中，集群路径规划以最大化信息增益为目标，持续优化对位置偏差分布的理解。在仿真环境中评估了SBE与IPP，并与开环策略进行对比，验证了方法的有效性。
原文摘要 · Abstract (English)
External factors, including urban canyons and adversarial interference, can lead to Global Positioning System (GPS) inaccuracies that vary as a function of the position in the environment. This study addresses the challenge of estimating a static, spatially-varying error function using a team of robots. We introduce a State Bias Estimation Algorithm (SBE) whose purpose is to estimate the GPS biases. The central idea is to use sensed estimates of the range and bearing to the other robots in the team to estimate changes in bias across the environment. A set of drones moves in a 2D environment, each sampling data from GPS, range, and bearing sensors. The biases calculated by the SBE at estimated positions are used to train a Gaussian Process Regression (GPR) model. We use a Sparse Gaussian process-based Informative Path Planning (IPP) algorithm that identifies high-value regions of the environment for data collection. The swarm plans paths that maximize information gain in each iteration, further refining their understanding of the environment's positional bias landscape. We evaluated SBE and IPP in simulation and compared the IPP methodology to an open-loop strategy.
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