Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing
This work addresses the problem of estimating the relative pose and velocity of a moving target using an IMU-equipped observer, assuming availability of either relative position or bearing measurements. By modeling the relative dynamics on the SE₂(3) Lie group and embedding them into ℝ¹⁵ to form a linear time-varying system, the authors propose a hybrid estimation architecture that combines a Riccati observer with an SO(3) nonlinear complementary filter. This approach achieves, for the first time within the SE₂(3) framework, a unified fusion of dual-IMU and relative sensing data, establishing a consistency observability condition that relies solely on target acceleration excitation. The method theoretically guarantees global exponential convergence of the estimation error and almost global asymptotic stability of the orientation estimate. Numerical simulations validate the effectiveness of the proposed technique.