  We present a novel self-supervised algorithm named MotionHint for monocular
visual odometry (VO) that takes motion constraints into account. A key aspect
of our approach is to use an appropriate motion model that can help existing
self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to
the local minima within their self-supervised loss functions. The motion model
is expressed with a neural network named PPnet. It is trained to coarsely
predict the next pose of the camera and the uncertainty of this prediction. Our
self-supervised approach combines the original loss and the motion loss, which
is the weighted difference between the prediction and the generated ego-motion.
Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint
algorithm on the standard KITTI benchmark. Experimental results show that our
MotionHint algorithm can be easily applied to existing open-sourced
state-of-the-art SSM-VO systems to greatly improve the performance by reducing
the resulting ATE by up to 28.73%.
