Quality-aware visual-inertial GPS/SINS coarse alignment in a transverse frame for robust polar navigation
摘要
Polar navigation with low-cost strapdown inertial navigation systems (SINS) is challenged by weak heading observability, GPS velocity degradation, and visually difficult scenes containing weak snow texture, strong ice reflection, low visibility, and motion blur. This paper proposes a learning-based quality-aware visual-inertial GPS/SINS coarse alignment method in the transverse frame. Image feature tracking and IMU preintegration are used to form short-window visual-inertial relative-motion constraints, which are mapped into auxiliary transverse-frame velocity and attitude observations. A closed-loop Kalman filtering structure jointly estimates the time-varying body-frame attitude relationship and gyroscope bias, and feeds the corrected states back to the velocity-vector construction process. To prevent unreliable visual observations from degrading the alignment filter, a lightweight learned quality model predicts the visual-inertial measurement weight from feature statistics, inlier ratio, reprojection error, image-degradation indicators, and preintegration residuals. Under degraded GPS observations, the proposed method reduces the heading RMSE from