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

MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints