Global RTK Positioning in Graphical State Space

TL;DR

This paper introduces a novel RTK post-processing method using a graphical state space model and factor graph optimization, outperforming traditional Kalman filter approaches in accuracy and effectiveness.

Contribution

It presents a new approach that models RTK positioning with a graphical state space and solves it via factor graph optimization, differing from traditional Kalman filter methods.

Findings

Factor graph optimization improves RTK post-processing accuracy.

Graphical state space model handles constant variables effectively.

Experimental results show superiority over traditional Kalman filter methods.

Abstract

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.