Aim of intense research in the field computational vision, dense 3D reconstruction achieves an important landmark with first methods running in real time with millimetric precision, using RGBD cameras and GPUs. However, these methods are not suitable for low computational resources. The goal of this work is to show a method of visual odometry using regular cameras, without using a GPU. The proposed method is based on techniques of sparse Structure from Motion (SFM), using data provided by dense 3D reconstruction. Visual odometry is the process of estimating the position and orientation of an agent (a robot, for instance), based on images. This paper compares the proposed method with the odometry calculated by Kinect Fusion. Odometry provided by this work can be used to model a camera position and orientation from dense 3D reconstruction.