  We propose a novel monocular visual odometry (VO) system called UnDeepVO in
this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera
and the depth of its view by using deep neural networks. There are two salient
features of the proposed UnDeepVO: one is the unsupervised deep learning
scheme, and the other is the absolute scale recovery. Specifically, we train
UnDeepVO by using stereo image pairs to recover the scale but test it by using
consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss
function defined for training the networks is based on spatial and temporal
dense information. A system overview is shown in Fig. 1. The experiments on
KITTI dataset show our UnDeepVO achieves good performance in terms of pose
accuracy.
