UAV Pose Estimation in GNSS-Denied Environment Assisted by Satellite Imagery Deep Learning Features

With the growing maturity of unmanned aerial vehicle (UAV) technology, its applications have widened to many spheres of life. The prerequisite for a UAV to perform air tasks smoothly is an accurate localization of its own position. Traditional UAV navigation relies on the Global Navigation Satellite System (GNSS) for localization; however, this system has disadvantages of instability and susceptibility to interference. Therefore, to obtain accuracy in UAV pose estimation in GNSS-denied environments, a UAV localization method that is assisted by deep learning features of satellite imagery is proposed. With the inclusion of a top-view optical camera to the UAV, localization is achieved based on satellite imageries with geographic coordinates and a digital elevation model (DEM). By utilizing the difference between the UAV frame and satellite imagery, the convolutional neural network is used to extract deep learning features between the two images to achieve stable registration. To improve the accuracy and robustness of the localization method, a local optimization method based on bundle adjustment (BA) is proposed. Experiments demonstrate that when the UAV's relative altitude is 0.5 km, the average localization error of this method under different trajectories is within 15 m.

Automated summary

With the growing maturity of UAV technology, a localization method based on deep learning features of satellite imagery is proposed to improve accuracy in pose estimation in GNSS-denied environments. This method utilizes a top-view optical camera, satellite imageries with geographic coordinates, and a digital elevation model for stable registration, and includes a local optimization method based on bundle adjustment for improved accuracy and robustness. Experiment results show an average localization error within 15 meters under different trajectories when the UAV's relative altitude is 0.5 km.