Vision systems are nowadays very promising for many on-board vehicles perception functionalities, like obstacles detection/recognition and ego-localization. In this paper, we present a 3D visual odometric method that uses a stereo-vision system to estimate the 3D ego-motion of a vehicle in outdoor road conditions. In order to run in real-time, the studied technique is sparse meaning that it makes use of feature points that are tracked during several frames. A robust scheme is also employed to reject outliers that are detected on moving objects of the environment. Moreover, efforts have been spent on the realtime implementation of the method. In this article, we describe the key stages of the method: features extraction and tracking, quadrifocal constraints, optimization solver and robustification. Real experiments are reported to compare the performance of this approach with GPS data and 2D-wheel-based odometry.