  This paper studies monocular visual odometry (VO) problem. Most of existing
VO algorithms are developed under a standard pipeline including feature
extraction, feature matching, motion estimation, local optimisation, etc.
Although some of them have demonstrated superior performance, they usually need
to be carefully designed and specifically fine-tuned to work well in different
environments. Some prior knowledge is also required to recover an absolute
scale for monocular VO. This paper presents a novel end-to-end framework for
monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).
Since it is trained and deployed in an end-to-end manner, it infers poses
directly from a sequence of raw RGB images (videos) without adopting any module
in the conventional VO pipeline. Based on the RCNNs, it not only automatically
learns effective feature representation for the VO problem through
Convolutional Neural Networks, but also implicitly models sequential dynamics
and relations using deep Recurrent Neural Networks. Extensive experiments on
the KITTI VO dataset show competitive performance to state-of-the-art methods,
verifying that the end-to-end Deep Learning technique can be a viable
complement to the traditional VO systems.
