  Classical monocular vSLAM/VO methods suffer from the scale ambiguity problem.
Hybrid approaches solve this problem by adding deep learning methods, for
example by using depth maps which are predicted by a CNN. We suggest that it is
better to base scale estimation on estimating the traveled distance for a set
of subsequent images. In this paper, we propose a novel end-to-end many-to-one
traveled distance estimator. By using a deep recurrent convolutional neural
network (RCNN), the traveled distance between the first and last image of a set
of consecutive frames is estimated by our DistanceNet. Geometric features are
learned in the CNN part of our model, which are subsequently used by the RNN to
learn dynamics and temporal information. Moreover, we exploit the natural order
of distances by using ordinal regression to predict the distance. The
evaluation on the KITTI dataset shows that our approach outperforms current
state-of-the-art deep learning pose estimators and classical mono vSLAM/VO
methods in terms of distance prediction. Thus, our DistanceNet can be used as a
component to solve the scale problem and help improve current and future
classical mono vSLAM/VO methods.
