The present thesis is concerned with the problem of robust pose estimation for planar targets in the context of real-time mobile vision. As a consequence of this research, individual developments made in isolation by earlier researchers are here considered together. Several adaptations to the existing algorithms are undertaken yielding a unified framework for robust pose estimation. This framework is specifically designed to meet the growing demand for fast and robust estimation on power-constrained platforms. For robust recognition of targets at very low computational costs, we employ feature based methods which are based on local binary descriptors allowing fast feature matching at run-time. The matching set is then fed to a robust parameter estimation algorithm in order to obtain a reliable homography. On the basis of our experimental results, it can be concluded that reliable homography estimates can be obtained using a device-friendly implementation of the Gaussian Elimination algorithm. We also show in this thesis that our simplified approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. The author's attention is focused not only on developing fast algorithms for the recognition framework but also on the optimized implementation of such algorithms. Any other recognition framework would similarly benefit from our optimized implementation.