Small-Error Linearized S-MSCKF with Loop-Closure-Triggered Global Reference Frame Redefinition

Accurate and robust localization is essential for the safe and efficient operation of autonomous systems. Visual-inertial navigation systems (VINS), which integrate camera and inertial measurement unit (IMU) data, are widely used for this purpose. The loop closure detection and update module in VINS contributes to the correction of global accumulated errors, whereas the traditional Multi-State Constraint Kalman Filter (MSCKF) method and its variants lack in-depth investigation on loop closure mechanisms. Besides, standard loop closure updates typically operate in a drifted global frame. It violates the small-error linearization assumption of Extended Kalman Filter (EKF) and thereby degrading correction accuracy. This paper is aimed at proposing a loop closure strategy that redefines the global reference frame prior to loop closure up dates, effectively resetting the EKF linearization point. Theoretical analysis shows that this reduces second-order linearization error, thereby enhancing estimation accuracy. Moreover, the filter's state vector is augmented with the gravity vector, and the filter performs high-precision loop closure update. Finally, experimental validation on public datasets and self-collected sequences demonstrates that both contributions improve localization accuracy.