An unmanned aerial system capable of finding world coordinates of a ground target is proposed here. The main focus here was to provide effective methodology to estimate ground target world coordinates using aerial images captured by the custom made micro aerial vehicle (MAV) as a part of visual odometery process on real time. The method proposed here for finding target's ground coordinates uses a monocular camera which is placed in MAV belly in forward looking/ Downward looking mode. The Binary Robust Invariant Scalable Key points (BRISK) algorithm was implemented for detecting feature points in the consecutive images. After robust feature point detection, efficiently performing Image Registration between the aerial images captured by MAV and with the Geo referenced images is the prime and core computing operation considered. Precise Image alignment is implemented by accurately estimating Homography matrix. In order to accurately estimate Homography matrix which consists of 9 parameters, this algorithm solves the problem in a Least Square Optimization way. Therefore, this framework can be integrated with visual odometery pipeline; this gives the advantage of reducing the computational burden on the hardware. The system can still perform the task of target geo-localization efficiently based on visual features and geo referenced reference images of the scene which makes this solution to be found as cost effective, easily implementable with robustness in the output. The hardware implementation of MAV along with this dedicated system which can do the proposed work to find the target coordinates is completed. The main application of this work is in search and rescue operations in real time scenario. The methodology was analyzed and executed in MATLAB before implementing real time on the developed platform. Finally, three case studies with different advantages derived from the proposed framework are represented.
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