Locate Where You Are by Block Joint Learning Network

Unmanned aerial vehicles (UAVs) are widely applied in various fields, which is located by the global position system (GPS) in most cases. However, in the GPS-denied cases, visual localization becomes very important. In complicated environments, such as weak illumination and ground objects changed, visual localization is unstable. This letter presents a UAV visual localization model with a block joint learning network (BJN). Different from the traditional feature extraction-comparison paradigm, the proposed BJN extracts the joint features of the input images at the same time, so as to fully mine the coupling relationship between the multi-source images. Besides this, to overcome the challenges of inconsistency style changes in image matching, the saliency feature based on the attention mechanism and the traditional edge feature operator are introduced in joint feature learning. To evaluate the performance of the proposed model, the experiments on the simulated dataset and the real dataset are given. Both the results on simulated and real datasets indicate that the proposed model is effective on multi-source image matching and UAV visual localization.