  In this paper we deal with the problem of odometry and localization for
Lidar-equipped vehicles driving in urban environments, where a premade target
map exists to localize against. In our problem formulation, to correct the
accumulated drift of the Lidar-only odometry we apply a place recognition
method to detect geometrically similar locations between the online 3D point
cloud and the a priori offline map. In the proposed system, we integrate a
state-of-the-art Lidar-only odometry algorithm with a recently proposed 3D
point segment matching method by complementing their advantages. Also, we
propose additional enhancements in order to reduce the number of false matches
between the online point cloud and the target map, and to refine the position
estimation error whenever a good match is detected. We demonstrate the utility
of the proposed LOL system on several Kitti datasets of different lengths and
environments, where the relocalization accuracy and the precision of the
vehicle's trajectory were significantly improved in every case, while still
being able to maintain real-time performance.
