Assessments of Nonlinear Least Squares Methods for Uav Vision Based Navigation

In recent years, the UAV’s are increasingly becoming main part of both military and commercial operations, where accurate pose estimation is considered as a critical problem to be investigated. UAV pose estimation problem can be investigated through vision based navigation (VBN) approach where visual sensors are augmented with the traditional IMU unit. VBN is based on localizing set of features (with known coordinates) on the ground and find their matches in the image taken by an imaging sensor on the UAV. Then, through Collinearity equation, object space transformation parameters are estimated such that these matches are transformed into position information. Two major problems in VBN are addressed. The first one is when general tilted aerial platform is used which leads to general photograph without the near vertical assumption. The limitations in traditional least square approach for solving the collinearity equation to deal with this situation have been presented and the solution to this problem is introduced using five different non-linear least squares methods. These methods are Trust region, Trust region dogleg algorithm, Levenberg-Marquardt, Nelder-Mead simplex direct search, and Quasi-Newton line search method. The second one is number of matches necessary for solving the collinearity equation which is highly required to be minimal as possible to save the cost needed for pre surveyed landmarks before the mission. This problem is addressed using the proposed nonlinear least squares methods mentioned above. Assessment of these methods in employing the SURF 64 and SURF 128 algorithm for image matching is investigated.