Advances in Simultaneous Localization and Mapping (SLAM) for Autonomous Mobile Robot Navigation

In robotics and autonomous systems, a fundamental problem known as simultaneous localization and mapping (SLAM) seeks to allow a robot to both map an unknown environment and localize itself inside it. SLAM is a positioning and localization technique that has its conceptual roots in mapping and navigation. It outperforms some traditional approaches because it enables more reliable and robust localization, planning, and control—all of which are crucial for autonomous vehicles. Over the years, numerous approaches have been developed to tackle this challenging task, each with its strengths and limitations. In this paper, we provide a concise overview of prominent SLAM implementation approaches/branches and their key characteristics, highlighting their distinct methodologies and applications. Each approach has its advantages and challenges, making the choice of the SLAM method dependent on the specific robotic platform, sensor suite, and environmental conditions. As SLAM continues to evolve, the integration of multiple approaches, sensor modalities, and the incorporation of machine learning techniques are likely to drive further advancements in the field, facilitating the establishment of more capable and robust autonomous systems.