A Comprehensive Study on Simultaneous Localization and Mapping (SLAM): Types, Challenges and Applications

Abstract

Simultaneous Localization and Mapping (SLAM) has emerged as a pivotal research area within the realm of artificial intelligence mobile robots. Its significance lies in the ability to facilitate self-exploration of unknown environments without the aid of human intervention. The unique and distinctive aspect of SLAM is its ability to map and localize the robot’s surroundings recursively and continuously. With the advent of SLAM, numerous algorithms have been proposed to address the challenges encountered in real-world applications of this technique. The main aim of this research is to provide a comprehensive and thorough analysis of the theoretical underpinnings, recent advancements, distinctive features, implementation strategies, and emerging issues in the domain of SLAM. This exploration intends to enhance the understanding and promote the advancement of this critical research area.