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Pose estimation is important for robotic perception, path planning, etc.Robot poses can be modeled on matrix Lie groups and are usually estimated viafilter-based methods. In this paper, we establish the closed-form formula forthe error propagation for the Invariant extended Kalman filter (IEKF) in thepresence of random noises and apply it to vision-aided inertial navigation. Weevaluate our algorithm via numerical simulations and experiments on theOPENVINS platform. Both simulations and the experiments performed on the publicEuRoC MAV datasets demonstrate that our algorithm outperforms some state-of-artfilter-based methods such as the quaternion-based EKF, first estimates JacobianEKF, etc.

Closed-form Error Propagation on the SE_n(3) Group for Invariant Extended Kalman Filtering with Applications to VINS