Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles

5 May 2019 Chengze Wang Yuan Yuan Qi. Wang

In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles. When modelling the geometric consistency within adjacent frames, most deep VO methods ignore the temporal continuity of the camera pose, which results in a very severe jagged fluctuation in the velocity curves... (read more)

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