This paper proposes several techniques for simultaneous localization and mapping of the environment, which allow mobile robots to self-reference themselves in a navigation environment with reduced accessibility by external means, such as the Global Position System (GPS). The methodology consists of implementing four unsupervised machine learning algorithms, using data sets generated based on a cloud of range points delivered by LiDAR sensor measurements. The proposed approach identifies characteristics from a navigability map, whereas an additional method based on Extended Kalman Filter (EKF) allows to find the robot positioning in conjunction. The proposed approach identifies navigability map features and an additional method based on EKF. EKF allows finding the robot positioning that is conjugated with each of the proposed algorithms. The first proposed method consists of estimating the features of the environment using heuristic methods and shaping the map using geometric principles. The second method is based on K-Means to incorporate the uncertainty in the sensor measurement, while the third solution uses the Gaussian Mixture Model (GMM). The fourth method focuses on Density-Based Spatial Clustering (DBSCAN). An odometry error is induced in the robot to include uncertainty within the test environment, which propagates into the positioning readings. The results show that DBSCAN presents a better execution time for the proposed localization system than the other comparative methods. Additionally, the robot’s localization is more accurate with this method, showing a 5% reduction of the error compared to the result obtained from the other proposed algorithms. Finally, with the results achieved, it is expected that the consumption of robot resources can be reduced with the reduction of localization error and automatic mapping.