It is shown how dense optical flow obtained using deep learning can be used to provide high quality visual odometry. The obtained odometric information can be utilized as a component to reduce the inherent drift of inertial navigation systems (INS). This could be a key component to provide autonomous system with robust localization capability in GNSS denied environments. The method leverages the power of estimating optical flow from neural networks, which can provide reliable results even when feature based optical flow fails. Comparison of different methods to decide which points of the dense optical flow that should be used to provide as good visual odometry as possible has been performed. Furthermore, it is exemplified how the methodology can help limit the drift in an INs.