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GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry
Neural inertial odometry increasingly uses networks as learned measurements inside filtering pipelines. Such measurements should transform consistently under arbitrary IMU mounting conventions: their mean must transform as a vector, and their covariance must transform congruently as a second-order t
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