A Novel Factor Graph Framework for Tightly Coupled GNSS/INS Integration With Carrier-Phase Ambiguity Resolution
Zhiheng Shen, Xingxing Li, Xuanbin Wang, Zongzhou Wu, Xin Li, Yuxuan Zhou, Shengyu Li
Abstract
Accurate position, velocity and orientation are essential for the autonomous navigation of unmanned vehicles. The integration of GNSS and INS that can deliver continuous navigation states is widely used for intelligent vehicle systems. In this paper, we propose a tightly coupled GNSS/INS positioning framework with carrier-phase ambiguity resolution based on factor graph optimization (FGO). In this approach, a sliding window optimizer is employed to fuse the multi-GNSS pseudorange and carrier-phase observations with inertial measurements. The same ambiguity within the window is considered as a state node, and the constraint on the ambiguity is continuously preserved by marginalization. To further improve the accuracy and reliability of precise positioning, the carrier-phase ambiguity resolution is introduced to the FGO-based GNSS/INS framework. When the vehicle is detected to be stationary, a zero-velocity constraint and an attitude invariant constraint will be imposed. Several experimental results indicate that the proposed method can accomplish the centimeter-level position estimation performance with beyond 90% positioning availability (horizontal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 10 cm and vertical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 10 cm) and outperforms the current state-of-the-art filter-based tightly coupled method.