  In this paper we present a data-driven approach to obtain the static image of
a scene, eliminating dynamic objects that might have been present at the time
of traversing the scene with a camera. The general objective is to improve
vision-based localization and mapping tasks in dynamic environments, where the
presence (or absence) of different dynamic objects in different moments makes
these tasks less robust. We introduce an end-to-end deep learning framework to
turn images of an urban environment that include dynamic content, such as
vehicles or pedestrians, into realistic static frames suitable for localization
and mapping. This objective faces two main challenges: detecting the dynamic
objects, and inpainting the static occluded back-ground. The first challenge is
addressed by the use of a convolutional network that learns a multi-class
semantic segmentation of the image. The second challenge is approached with a
generative adversarial model that, taking as input the original dynamic image
and the computed dynamic/static binary mask, is capable of generating the final
static image. This framework makes use of two new losses, one based on image
steganalysis techniques, useful to improve the inpainting quality, and another
one based on ORB features, designed to enhance feature matching between real
and hallucinated image regions. To validate our approach, we perform an
extensive evaluation on different tasks that are affected by dynamic entities,
i.e., visual odometry, place recognition and multi-view stereo, with the
hallucinated images. Code has been made available on
https://github.com/bertabescos/EmptyCities_SLAM.
