LF-GNSS integrates a learned measurement-noise model and innovation correction into an extended Kalman filter, using a DOP-based satellite feature and a hard-example-mining loss, and reports improved urban GNSS positioning accuracy.
R2-gvio: A robust, real-time gnss-visual-inertial state estimator in urban challenging environments,
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LF-GNSS: Towards More Robust Satellite Positioning with a Hard Example Mining Enhanced Learning-Filtering Deep Fusion Framework
LF-GNSS integrates a learned measurement-noise model and innovation correction into an extended Kalman filter, using a DOP-based satellite feature and a hard-example-mining loss, and reports improved urban GNSS positioning accuracy.