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Repository Details
The method proposed here uses University of Toronto (STARS Lab.) Foot-Mounted Inertial Navigation Dataset (VICON room experiments) to build a modern (data-driven) end-to-end INS (LLIO) and integrates it with an improved version of PyShoe, which is a robust ZUPT aided Error-State Kalman Filter based INS.
