Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds

Abstract

Localization and Mapping is an essential compo-nent to enable Autonomous Vehicles navigation, and requiresan accuracy exceeding that of commercial GPS-based systems.Current odometry and mapping algorithms are able to providethis accurate information. However, the lack of robustness ofthese algorithms against dynamic obstacles and environmentalchanges, even for short time periods, forces the generationof new maps on every session without taking advantage ofpreviously obtained ones. In this paper we propose the useof a deep learning architecture to segmentmovableobjectsfrom 3D LiDAR point clouds in order to obtain longer-lasting3D maps. This will in turn allow for better, faster and moreaccurate re-localization and trajectoy estimation on subsequentdays. We show the effectiveness of our approach in a verydynamic and cluttered scenario, a supermarket parking lot.For that, we record several sequences on different days andcompare localization errors with and without ourmovableobjects segmentation method. Results show that we are able toaccurately re-locate over a filtered map, consistently reducingtrajectory errors between an average of35.1% with respectto a non-filtered map version and of47.9% with respect to astandalone map created on the current session.