IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments

Zijia Shi

doi:10.47297/wspciWSP2516-252701.20261004

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IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments

Volume 10, Issue 4

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IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments
IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments
School of Physics and Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong, 510632, China
Vol. 10, Issue 4, Pages: 247-252(2026)
Published：30 August 2026
DOI：10.47297/wspciWSP2516-252701.20261004
Accepted：

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Shi Zijia.IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments[J].Creativity and Innovation,2026,10(04):247-252.
DOI：

Shi Zijia.IMU/Optical Flow Fusion Estimation and Disturbance-Rejection Control for Quadrotor UAVs in GNSS-Denied Environments[J].Creativity and Innovation,2026,10(04):247-252. DOI： 10.47297/wspciWSP2516-252701.20261004.

摘要

Abstract

This paper addresses the cooperative design of inertial measurement unit (IMU) and optical flow sensor fusion-based state estimation and disturbance-rejection control for quadrotor UAVs operating in indoor and near-ground environments where global navigation satellite system (GNSS) signals are unavailable. IMU provides high-frequency continuous measurements but suffers from cumulative drift

while optical flow sensors are low-cost and compact yet sensitive to ground texture and imaging quality; their characteristics are thus mutually complementary. Existing approaches typically design the estimator and controller independently

lacking a coordinated adjustment mechanism for optical flow degradation scenarios. This paper proposes an adaptive IMU/optical flow tightly coupled estimation and disturbance observation compensation cooperative framework. At the estimation layer

a fusion estimator based on error-state Kalman filtering (ESKF) is constructed; it computes a multi-layer optical flow quality factor online by integrating image feature count

motion consistency

and filter residual statistics

and dynamically adjusts observation noise weights accordingly. At the control layer

a nonlinear disturbance observer is designed to estimate and feedforward-compensate unknown wind disturbances and aerodynamic uncertainties online

with the observer bandwidth linked to the estimation confidence level

forming a cooperative control mechanism that aggressively compensates when confidence is high and conservatively suppresses when confidence is low. The uniform ultimate boundedness of the closed-loop tracking error is proved using the Lyapunov method. MATLAB/Simulink simulation results demonstrate that the proposed method outperforms fixed-parameter fusion and conventional PID baselines in hovering disturbance rejection

trajectory tracking

and optical flow degradation scenarios

and is practically feasible for integration into existing flight controller frameworks.

关键词

Keywords

references

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