Simultaneous Localisation and Mapping of a Mobile Robot in a Static Environment

Abstract

In the realm of robotics and AI, the Simultaneous Localization And Mapping (SLAM) problem pertains to enabling an autonomous robot to map its surroundings while maintaining awareness of its global position and movement during navigation. This project delves into SLAM within the confines of a static environment, examining four chosen algorithms: Graph-SLAM, EKF-SLAM, Fast-SLAM, and ICP-SLAM. The study involves both theoretical analysis and algorithmic comparisons, which are drawn from previous research. Practical simulations are employed to evaluate the algorithms based on speed, efficiency, and accuracy. The results obtained are then compared with literature to test their consistency. The study culminates in the development of a practical SLAM algorithm for a mobile robot, which draws inspiration from the investigated algorithms, and is rigorously tested across diverse scenarios, followed by resource analysis and different map configurations.