Invariant Extended Kalman Filtering Using Two Position Receivers for Extended Pose Estimation
This work addresses pose estimation for rigid bodies in navigation or robotics, but it is incremental as it builds on existing IEKF methods by incorporating two receivers.
This paper tackled the problem of estimating the extended pose (position, velocity, and attitude) of a rigid body using two position receivers and an IMU, and the result was that the proposed two-receiver invariant extended Kalman filter (IEKF) yielded improved estimates over alternative filters, as demonstrated in Monte Carlo simulations and an experiment.
This paper considers the use of two position receivers and an inertial measurement unit (IMU) to estimate the position, velocity, and attitude of a rigid body, collectively called extended pose. The measurement model consisting of the position of one receiver and the relative position between the two receivers is left invariant, enabling the use of the invariant extended Kalman filter (IEKF) framework. The IEKF possesses various advantages over the standard multiplicative extended Kalman filter, such as state-estimate-independent Jacobians. Monte Carlo simulations demonstrate that the two-receiver IEKF approach yields improved estimates over a two-receiver multiplicative extended Kalman filter (MEKF) and a single-receiver IEKF approach. An experiment further validates the proposed approach, confirming that the two-receiver IEKF has improved performance over the other filters considered.