A Factor Graph Optimization Method with Initial Velocity Bias Estimation for GINS Initial Alignment

Initial alignment is a crucial stage in navigation because it directly determines the navigation accuracy of the global positioning system (GPS)-inertial navigation system (GINS) that consists of the GPS and the strapdown inertial navigation system. However, GPS outliers and their resultant initial velocity bias error, both caused by the GPS signal blockage and/or reflection, severely degrade the GINS initial alignment accuracy. In addition, the inertial measurement unit (IMU) bias can also cause the cumulative bias error to adversely affect the alignment accuracy. In order to simultaneously suppress these errors for the GINS initial alignment, we propose a factor graph optimization (FGO) method that models the initial velocity bias as a state. Specifically, the IMU bias and the initial velocity bias are estimated and then compensated to improve the alignment accuracy. The Huber norm is employed to suppress the GPS outliers and thus the stable alignment process can be achieved. A global observability analysis is conducted, showing that the newly augmented initial velocity bias, together with the other estimated states, is observable when the vehicle trajectory contains a constant-attitude straight-line motion interval during which the specific-force derivative vectors at two distinct instants are linearly independent. The car-mounted experiment results demonstrate that the proposed FGO method can effectively suppress the aforementioned errors.