Self-localization of UAVs based on visual-inertial integrated navigation

Abstract

With the increasing application of unmanned aerial vehicles (UAVs) in all walks of life, the demand for autonomous navigation of UAVs has become increasingly urgent, especially in areas where there is no GPS signal or where the GPS signal is interfered with. This paper proposes an integrated navigation method based on visual navigation and inertial navigation. First, it leverages the satellite map as the reference map. During the flight of the UAV, the camera takes pictures of the ground at intervals. And then it matches the photo of the UAV and the reference map. In this way, the visual navigation system localizes the UAV and evalutate the the reliability of the localization. Finally the Particle Filter algorithm is introduced to fuse the positioning results. To expedite the matching process, we utilize the INS to narrow down the range of satellite maps for matching. Considering the differences between the camera photo and the reference map, this paper introduces SuperPoint and SuperGlue algorithms for feature extraction and matching, respectively. These two algorithms utilize deep neural networks to extract and match the features of the images, enabling the extraction of deep semantic features rather than manual features. Experiment results demonstrate the superior matching effectiveness of the image matching algorithm introduced in this paper. The simulation results show that the cumulative error of INV is greatly reduced after fusing with the results of visual navigation. Since it navigates autonomously, the integrated navigation method offers robust anti-interference capabilities, high autonomy, and better adaptability.