The robot to have the ability to move within an environment without the assistance of a human being, it is required to have a knowledge of the environment and its location within it at the same time. In many robotic applications, it is not possible to have an a priori map of the environment. In that situation, the robot needs to build a local map of its environment while executing its mission and, simultaneously, determine its location. A typical solution for the Simultaneous Localization and Mapping (SLAM) problem primarily uses two types of sensors: i) an odometer that provides information of the robot’s movement and ii) a range measurement that provides perception of the environment. In this work, a solution for the SLAM problem is presented using a DP-SLAM algorithm purely based on laser readings, focused on structured indoor environments. It considers that the mobile robot only uses a single 2D Laser Range Finder (LRF), and the odometry sensor is replaced by the information obtained from the overlapping of two consecutive laser scans. The Normal Distributions Transform (NDT) algorithm of the scan matching is used to approximate a function of the map overlapping. To improve the performance of this algorithm and deal with low-quality range data from a compact LRF, a scan point resampling is used to preserve a higher point density of high information features from the scan. An evolution differential algorithm is presented to optimize the overlapping process of two scans. During the development of this work, the mobile robot iRobot Create, assembled with one LRF Hokuyo URG-04LX, is used to collect real data in several indoor environments, generating 2D maps presented as results.