论文标题
全局RTK位置在图形状态空间中
Global RTK Positioning in Graphical State Space
论文作者
论文摘要
本文提出了一种用于RTK后处理的新方法。与传统的前向kalman滤波器不同，在我们的方法中，整个系统方程式建立在图形状态空间模型上，并通过因子图优化解决。正向卡尔曼滤波器提供的位置解决方案用作图形状态空间模型的线性化点。恒定变量（例如双差歧义）将作为图形状态空间模型中的常数存在，而不是时间序列变量。通过实验结果表明，使用图形状态空间模型的因子图优化比Kalman滤波器更有效，而Kalman滤波器具有传统的离散时间状态空间模型，用于RTK后处理问题。
This paper proposes a new method for RTK post-processing. Different from the traditional forward-backward Kalman filter, in our method, the whole system equation is built on a graphical state space model and solved by factor graph optimization. The position solution provided by the forward Kalman filter is used as the linearization points of the graphical state space model. Constant variables, such as double-difference ambiguity, will exist as constants in the graphical state space model, not as time-series variables. It is shown by experiment results that factor graph optimization with a graphical state space model is more effective than Kalman filter with a traditional discrete-time state space model for RTK post-processing problem.
