Wind and State Estimation on SE(3): Comparative Evaluation of EKF and UKF with Continuous and Discrete Quadrotor Models

2026-06-29

AI Summary

This work addresses the challenge of jointly estimating wind velocity and vehicle state for quadrotor UAVs operating in SE(3) using only onboard sensors. The authors propose a discrete-time dynamics model based on Lie group variational integrators and, for the first time, apply it to wind estimation, thereby avoiding the approximation errors inherent in conventional discretization methods. Building upon this model, they develop a high-precision joint estimation framework on the SE(3) manifold by integrating both extended and unscented Kalman filters (EKF and UKF). Comprehensive simulations and real-world outdoor experiments demonstrate that the proposed discrete SE(3) model, particularly when coupled with the UKF, significantly outperforms continuous-time counterparts and maintains excellent estimation accuracy and trajectory tracking performance even with low-cost sensors.

Abstract

Use of quadrotor UAVs for wind velocity estimation is gaining popularity in recent studies, leveraging their maneuverability, compact size and low cost. Among available approaches, model-based wind velocity estimation is most commonly used, since it relies only on onboard sensors. However, as the quadrotor is a highly nonlinear system, thus making this task challenging. This study evaluate the use of both discrete and continuous dynamic equations of the quadrotor UAV for wind velocity estimation on SE(3), rather than commonly adapted continuous or discretized form. Lie Group Variational Integrator, developed on discrete Lagrangian is used as the discrete model without any approximation or discritization. The study assess both the discrete and continuous form of the quadrotor dynamics on SE(3) using Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). The quadrotor UAV performance is evaluated in both MATLAB-based numerical simulations and free outdoor flight. The numerical simulations are conducted during both hovering and trajectory-tracking flights. Results demonstrate that, by using discrete SE(3) dynamics coupled with UKF, the quadrotor achieves higher estimation accuracy while maintaining trajectory tracking, even with low-cost sensors. These findings highlight the potential of discrete quadrotor models with UKF not only for wind velocity estimation but also for other high-accuracy tasks, even when relying on low-cost onboard sensors.

Problem

• wind estimation
• quadrotor UAV
• SE(3)
• state estimation
• nonlinear system

Innovation

• SE(3)
• discrete variational integrator
• wind estimation
• UKF
• quadrotor dynamics