Abstract:

This Seminar presents an investigation of the performance of four Simultaneous Localization and Mapping (SLAM) systems: Cartographer SLAM, G-Mapping, Hector Mapping, and RTAB-Mapping. A comprehensive analysis of their performance in terms of accuracy, robustness, computational efficiency, and resource utilization is carried out. Hector mapping excelled in efficiency. G-mapping stood out when it comes to robustness to disturbances. RTAP map utilized deep learning in loop closure tasks to excel in creating large-scale and accurate maps, while Cartographer SLAM used a unique map dividing technique to create very precise maps. The study aims to work as a guide for researchers contemplating the use of any of those SLAM algorithms in their work.

Speaker Bio:

Mr. Sajjad A. Mahmoud is a Control Systems Engineer and is pursuing his M.Sc. degree in Systems & Control Engineering in the Control and Instrumentation Engineering Department at King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia. His graduate research integrates adaptive and optimal control frameworks with intelligent learning algorithms and computer vision–based monitoring to enhance stability and efficiency in renewable energy systems and smart mobility platforms. By leveraging real-time system identification and advanced simulation in MATLAB/Simulink, he develops data-driven control solutions for complex, nonlinear dynamic challenges.