One of the challenges in the automation of vehicles takes place in the car park. Automatic vehicles must be able to localize itself accurately and build a map of the car park in real time. This capability will be able to extend the range of possible applications including the tracking of car park lots availability, automatic navigation and automatic parking to name a few. In this project, we took on the task of localizing an automatic vehicle and building a map of the car park in real time. This project takes place within the car park of INRIA Rhone-Alpes on the CyCab vehicle with a Sick laser range scanner. A key feature is that it works only with laser scanners to retrieve the position and orientations of vehicles in the car park. With the detected vehicles as landmarks, CyCab performs a localization of itself and build a map of the car park at the same time. This problem is commonly known in the literature as Simultaneous Localization and Mapping(SLAM). The detection of vehicles is based on the work of a previous DEA student (Lorieux) and extensions were proposed to increase the reliability of vehicle detection. The SLAM algorithm chosen is the FastSLAM algorithm. The FastSLAM algorithm is adapted within this context. However, FastSLAM only gives a set of hypotheses. Hence, a map construction method is proposed to merge the different hypotheses together to form one single final map. Experiments with real data were conducted and it is able to perform the tasks required. The experiments also revealed weaknesses in the system and possible approaches and directions for further research is suggested.