Analysis of GPS/IMU Sensor Fusion to Improve Mapping Accuracy on UAV Quadrotor Using LiDAR Technology

Abstract

Unmanned Aerial Vehicles (UAVs) play a crucial role in navigation, requiring accurate sensors to determine position, speed, and orientation, especially in unknown environments. Direct navigation systems like the Global Positioning System (GPS) provide positional data, while indirect systems, such as the Inertial Measurement Unit (IMU), integrate accelerometer and gyroscope data to supply speed and orientation information. This study investigates the integration of GPS and IMU sensors using the Unscented Kalman Filter (UKF) to improve localization accuracy on a cost-effective UAV Quadrotor equipped with LiDAR Livox. The research methodology involved collecting raw data from GPS, IMU, and LiDAR sensors during UAV flights. These data were processed using a UKF-based mathematical model to fuse sensor inputs and generate accurate point cloud models. Results show that the UKF fusion method achieved a localization accuracy of 0.403 m, with maximum residuals recorded as 1.332 m for the X axis, 20.421 m for the Y axis, and 4.385 m for the Z axis, significantly outperforming standalone GPS systems. Additionally, the UKF improved the precision of 3D LiDAR point clouds, achieving an overall accuracy of 0.034 m, with specific axis accuracies of 0.007 m (X axis), 0.005 m (Y axis), and 0.032 m (Z axis). These advancements resulted in a denser point cloud, enhancing volumetric calculations by 28.92% compared to the Extended Kalman Filter (EKF). The study highlights the robustness of UKF in handling sensor noise and nonlinearities, making it a suitable approach for UAV navigation and mapping tasks. These findings support the broader application of GPS/IMU sensor fusion in cost-sensitive UAV systems, emphasizing its potential for environmental mapping, precision agriculture, and urban planning.