  In recent years, camera-based localization has been widely used for robotic
applications, and most proposed algorithms rely on local features extracted
from recorded images. For better performance, the features used for open-loop
localization are required to be short-term globally static, and the ones used
for re-localization or loop closure detection need to be long-term static.
Therefore, the motion attribute of a local feature point could be exploited to
improve localization performance, e.g., the feature points extracted from
moving persons or vehicles can be excluded from these systems due to their
unsteadiness. In this paper, we design a fully convolutional network (FCN),
named MD-Net, to perform motion attribute estimation and feature description
simultaneously. MD-Net has a shared backbone network to extract features from
the input image and two network branches to complete each sub-task. With
MD-Net, we can obtain the motion attribute while avoiding increasing much more
computation. Experimental results demonstrate that the proposed method can
learn distinct local feature descriptor along with motion attribute only using
an FCN, by outperforming competing methods by a wide margin. We also show that
the proposed algorithm can be integrated into a vision-based localization
algorithm to improve estimation accuracy significantly.
