Relative Pose Estimation Based on Pairwise Range With Application to Aerobridge

Abstract

Relative pose estimation refers to estimate the relative attitude and translation between multiple platforms. For mobile platforms, tracking the relative pose with pairwise range is challenging for highly nonlinear associations between measurement and state. This article proposes a promising framework using pairwise range to estimate the relative pose parameterized with Lie algebra. It is compatible with the existing Gauss-Newton method and the Levenberg-Marquardt method. We analyze the existence of the optimal solution based on the rank of the Hessian matrix, which turns into a discussion of sensors placement. The associated unconstrained Cramer-Rao Lower Bound with fewer variables is presented. To track moving platforms, we derived a novel and accurate relative kinematics without angular accelerations. An extended Kalman filter incorporating the measurement of an IMU is designed to generate smooth poses. A simplified version of the optimizer with less dimension is introduced to the application of aerobridge, which is also compatible with other multilink devices. Simulations verify the proposed algorithm and the comparisons with the existing popular methods prove its novelty.

Keywords

• Cramer-Rao lower bound
• extended Kalman filter
• nonlinear optimization
• relative pose estimation
• wireless sensors network