Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMUGPS Sensor Fusion and Data Filtering Algorithm

TL;DR

This paper introduces a software platform for UAV modeling that integrates sensor fusion of IMU and GPS data with filtering algorithms to achieve accurate, real-time orientation and position visualization.

Contribution

It presents a cost-effective, data-driven UAV modeling software utilizing sensor fusion and filtering techniques for improved accuracy and real-time performance.

Findings

High accuracy in UAV orientation and position visualization

Effective sensor fusion of IMU and GPS data

Real-time data updating during UAV flight

Abstract

Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity