Multi-sensor data fusion for autonomous flight of unmanned aerial vehicles in complex flight environments

The flight environment of unmanned aerial vehicles faces various challenges. To effectively navigate and perform tasks, they need to effectively integrate multiple sensors. This study applies the adaptive weighted average method, combined with data from global positioning system, inertial measurement unit, three-dimensional optical detection and ranging, and uses linear Kalman filtering to smooth the merged velocity data. High-order B-spline curves for route planning and applying flight constraint formulas to better adapt are used to the dynamics of unmanned aerial vehicles. The research results indicated that the improved adaptive weighting algorithm had high comprehensive performance for multi-sensor data fusion, with the highest accuracy, robustness, real-time performance, and consistency of 94.2%, 93.7%, 100%, and 95.6%, respectively. The flight path lengths planned by the A & lowast; algorithm and higher-order B-spline curve were 15.7 and 16.3 m, respectively, and the flight time was 8.2 and 7.1 s, respectively. The flight path planned by higher-order B-spline curve was further away from obstacles. The use of adaptive weighted fusion and linear Kalman filtering facilitates the fusion of multi-sensor data, and autonomous flight routes planned using high-order B-spline curves can also meet the needs of unmanned aerial vehicle flight in complex flight environments.