Real-Time UAV 3D Image Point Clouds Mapping

This paper proposes a real-time 3D image point clouds mapping algorithm for UAVs that is capable of mapping effectively in weak GNSS environments. And a UAV mapping system is integrated with a RGB camera, an inertial measurement unit (IMU), a GNSS receiver, data transmission devices, and a DJI M300 flight platform. To achieve real-time and robust mapping, the system utilizes a visual-inertial odometry (VIO) that tightly couples GNSS, RGB image, and IMU, which provides stable state estimation information for mapping. Subsequently, a dense matching algorithm based on key frames is adopted to recover 3D mapping information with low-computational cost. Extensive experiments are conducted on our test site, demonstrating the system's ability to build maps stably, even under the effect of wind. The results compared with the trajectory reconstructed by Pix4D show that the system achieves competitive accuracy of pose estimation and is capable of real-time mapping.