Simultaneous localization and Mapping with EKF and known correspondes

The Simultaneous Localization and Mapping (SLAM) algorithm is the basis for the most navigation systems in a large range of indoor, outdoor, air and underwater applications for both manned and autonomous vehicles. The purpose of this thesis is to investigate and build a model for the SLAM algorithm. Experiments are carried on the algorithm to analyze different values of the parameters and evaluate for which one of them the model gives the best results. The two main algorithms implemented in Python were the Extended Kalman Filter (EKF) algorithm with known correspondences and the Occupancy Grid Mapping algorithm. Several case studies with different parameter values for these algorithms were defined to conduct the research by performing simulations in the V-REP software. The obtained results prove that the built model meets the three important convergence properties of the EKF algorithm as a basis for solving the SLAM problem. Through a good estimation technique and making assumptions about the environment some systems can solve the problem quite well, but for now SLAM is still an open problem.