Calibration and Research of Unmanned Aerial Vehicle Sensors

Abstract

This paper focuses on the multi-sensor calibration problem in unmanned aerial vehicle obstacle-avoidance systems, systematically analysing the error characteristics and modelling differences among lidar, vision sensors, ultrasonic sensors, and millimetre-wave radar under dynamic flight conditions. It is pointed out that problems such as time asynchrony, external parameter drift and slow parameter failure caused by high-speed motion, vibration disturbance and environmental change of unmanned aerial vehicles make traditional static calibration difficult to meet the requirements of real-time obstacle avoidance. Subsequently, by sorting out the internal and external parameter calibration models, coordinate system transformation relationships and error modelling methods, it is proposed to incorporate the key calibration parameters into the online state estimation framework to achieve dynamic update and structural constraint compensation, and then combine the multi-sensor fusion calibration with the intelligent algorithm adaptive compensation mechanism to improve the robustness and consistency of the system in complex environments. Finally, the calibration strategies for typical scenarios such as urban environment, indoor navigation, disaster relief and agricultural operations were analysed. The paper explores the stable operation schemes of real-time obstacle avoidance systems for unmanned aerial vehicles, providing a theoretical basis and methodological support for engineering applications.