Equivariant Filtering Framework for Inertial-Integrated Navigation
This work addresses state estimation for inertial-integrated navigation, which is incremental as it builds on existing invariant filtering methods.
The paper tackles the inertial-integrated state estimation problem by proposing an equivariant filtering framework that exploits Lie group symmetries, resulting in an extension of invariant extended Kalman filtering with detailed analytic state transition matrices.
This paper proposes a equivariant filtering (EqF) framework for the inertial-integrated state estimation problem. As the kinematic system of the inertial-integrated navigation can be naturally modeling on the matrix Lie group $SE_2(3)$, the symmetry of the Lie group can be exploited to design a equivariant filter which extends the invariant extended Kalman filtering on the group affine system. Furthermore, details of the analytic state transition matrices for left invariant error and right invariant error are given.