Efficiently Kinematic-Constraint-Coupled State Estimation for Integrated Aerial Platforms in GPS-Denied Environments

Abstract

Small-scale autonomous aerial vehicles (AAVs) are widely used in various fields. However, their underactuated design limits their ability to perform complex tasks that require physical interaction with environments. The fully-actuated Integrated Aerial Platforms (IAPs), where multiple AAVs are connected to a central platform via passive joints, offer a promising solution. However, achieving accurate state estimation for IAPs in GPS-denied environments remains a significant hurdle. In this letter, we introduce a centralized state estimation framework for IAPs with a fusion of odometry and kinematics, using only onboard cameras and inertial measurement units (IMUs). We develop a forward-kinematic-based formulation to fully leverage localization information from kinematic constraints. An online calibration method for kinematic parameters is proposed to enhance state estimation accuracy with forward kinematics. Additionally, we perform an observability analysis, theoretically proving that these kinematic parameters are fully observable under conditions of fully excited motion. Dataset and real-world experiments on a three-agent IAP prototype confirm that our method improves localization accuracy and reduces drift compared to the baseline.