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Visual odometry has been an important research activity in the last three years. Because the results of ego-motion estimation tasks are used in complex systems which need to work real-time, the motion estimation itself need to perform faster than real-time such that the remaining time slots can be used by other algorithms running on the same hardware. The main contribution of this paper is the implementation of a GPU based method for 3D ego-motion estimation. We identified the visual odometry method that is the best candidate for parallelization and we describe the details of the parallel implementation. We also present different tests performed on various traffic scenes to show the robustness of the method and the performance compared to the sequential implementation.