This paper presents a method for localizing an Unmanned Aerial Vehicle (UAV) in indoor or outdoor environments. The approach has the ability to estimate the 3D pose of the on-board camera by using a Harris corner detector and the Levenberg-Marquardt (LM) with the Random Sample Consensus (RANSAC) algorithm to perform detection. The implementation of such computational intensive tasks in embedded system is necessary for the autonomy of UAV. Accelerators implemented on FPGA provide a solution to reach required performances. In addition to the algorithm development, we present the embedding of a real time camera pose estimation algorithm on a Xilinx System on Programmable Chip (SoPC) platform. Partitioning of our embedded application into hardware and software parts on a Zynq Board has significantly reduced the execution time when compared with software implementation, while offering necessary reconfiguration capabilities.