Data Fusion Techniques for Enhanced UAV Near-Ground Distance Measurement Using Point and Surface Sensors

Abstract

Accurate near-ground distance measurement is crucial for UAV navigation, particularly in complex environments. Traditional single-sensor systems face challenges in precision and real-time performance. This paper explores data fusion methods using laser point sensors and ultrasonic surface sensors to enhance measurement accuracy and stability. By comparing adaptive weighted fusion and Kalman filter algorithms, we identify the superior approach for combining point and surface sensor data. Experimental results demonstrate that the Kalman-based fusion algorithm significantly improves data accuracy, reduces mutation issues, and provides robust performance in agricultural UAV applications.