The corresponding matched images are hard to obtain by the convetional scene matching localization method applied in UAV (Unmanned Aerial Vehicle) at a low altitude because of the small viewing coverage and the big capturing angle difference comparing with the satellite images.We propose a localization method based on instant dense 3D reconstruction.Firstly

we use a fast SLAM (Simultaneous Localization And Mapping) method to retrieve camera poses of image sequence captured by UAV.Secondly

cooperative denoising and optimization algorithm across multiple key frames is applied to obtain dense depth map and dense point cloud.Thirdly

a virtual view

the angle of which is similar to that of satellite

is generated by iterative optimizing method.Finally

we estimate the position of UAV by correspondence between the satellite map and the previous generated virtual view.Since the dense 3D point cloud integrates the information of multiple aerial images with small field of view and the viewing angles of some generated virtual views are close to those of satellite images

the proposed method provides a higher success rate and accuracy for localization.Experimental results illustrate the effectiveness and applicability of the proposed framework.