Efficient 7D aerial pose estimation

Abstract

A method for online global pose estimation of aerial images by alignment with a georeferenced 3D model is presented. Motion stereo is used to reconstruct a dense local height patch from an image pair. The global pose is inferred from the 3D transform between the local height patch and the model. For efficiency, the sought 3D similarity transform is found by least-squares minimizations of three 2D subproblems. The method does not require any landmarks or reference points in the 3D model, but an approximate initialization of the global pose, in our case provided by onboard navigation sensors, is assumed. Real aerial images from helicopter and aircraft flights are used to evaluate the method. The results show that the accuracy of the position and orientation estimates is significantly improved compared to the initialization and our method is more robust than competing methods on similar datasets. The proposed matching error computed between the transformed patch and the map clearly indicates whether a reliable pose estimate has been obtained.