Self-driving car's navigation requires a very precise localization covering wide areas and long distances. Moreover, they have to do it at faster speeds than conventional mobile robots. This paper reports on an efficient technique to optimize the position of a sequence of maps along a journey. We take advantage of the short-term precision and reduced space on disk of the localization using 2D occupancy grid maps, from now on called sub-maps, as well as, the long-term global consistency of a Kalman filter that fuses odometry and GPS measurements. In our approach, horizontal planar LiDARs and odometry measurements are used to perform 2D-SLAM generating the sub-maps, and the EKF to generate the trajectory followed by the car in global coordinates. During the trip, after finishing each sub-map, a relaxation process is applied to a set of the last sub-maps to position them globally using both, global and map's local path. The importance of this method lies on its performance, expending low computing resources, so it can work in real time on a computer with conventional characteristics and on its robustness which makes it suitable for being used on a self-driving car as it doesn't depend excessively on the availability of GPS signal or the eventual appearance of moving objects around the car. Extensive testing has been performed in the suburbs and in the downtown of Nantes (France) covering a distance of 25 kilometers with different traffic conditions obtaining satisfactory results for autonomous driving.