  Localization and Mapping is an essential component to enable Autonomous
Vehicles navigation, and requires an accuracy exceeding that of commercial
GPS-based systems. Current odometry and mapping algorithms are able to provide
this accurate information. However, the lack of robustness of these algorithms
against dynamic obstacles and environmental changes, even for short time
periods, forces the generation of new maps on every session without taking
advantage of previously obtained ones. In this paper we propose the use of a
deep learning architecture to segment movable objects from 3D LiDAR point
clouds in order to obtain longer-lasting 3D maps. This will in turn allow for
better, faster and more accurate re-localization and trajectoy estimation on
subsequent days. We show the effectiveness of our approach in a very dynamic
and cluttered scenario, a supermarket parking lot. For that, we record several
sequences on different days and compare localization errors with and without
our movable objects segmentation method. Results show that we are able to
accurately re-locate over a filtered map, consistently reducing trajectory
errors between an average of 35.1% with respect to a non-filtered map version
and of 47.9% with respect to a standalone map created on the current session.
