Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing

2026-05-13

AI Summary

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.

Abstract

This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target, where both are equipped with Inertial Measurement Units (IMUs), assuming the availability of relative position or bearing measurements. The body-target relative dynamics are formulated on $\mathbf{SE}_2(3)$ and recast into a linear time-varying (LTV) model in the ambient space $\mathbb{R}^{15}$, on which a deterministic Riccati observer is designed. We analyze the uniform observability (UO) conditions required to guarantee global exponential convergence of the estimation error in the ambient space for both measurement cases. In the case of relative position measurements, UO requires only a persistence-of-excitation condition on the target acceleration, whereas for bearing measurements, additional conditions are required. Building on this, a nonlinear complementary filter on $\mathbf{SO}(3)$ is designed to provide a smooth estimate of the orientation component of the state with almost global asymptotic stability. Finally, simulation results are provided to validate the proposed solution.

Problem

• Relative Pose Estimation
• Velocity Estimation
• Dual IMU
• Relative Position Sensing
• Moving Target

Innovation

• SE2(3) observer
• dual IMU fusion
• relative pose-velocity estimation
• uniform observability
• nonlinear complementary filter