  Knowledge about the location of a vehicle is indispensable for autonomous
driving. In order to apply global localisation methods, a pose prior must be
known which can be obtained from visual odometry. The quality and robustness of
that prior determine the success of localisation. Momo is a monocular
frame-to-frame motion estimation methodology providing a high quality visual
odometry for that purpose. By taking into account the motion model of the
vehicle, reliability and accuracy of the pose prior are significantly improved.
We show that especially in low-structure environments Momo outperforms the
state of the art. Moreover, the method is designed so that multiple cameras
with or without overlap can be integrated. The evaluation on the KITTI-dataset
and on a proper multi-camera dataset shows that even with only 100--300 feature
matches the prior is estimated with high accuracy and in real-time.
