Advancement in SLAM Techniques and Their Diverse Applications

Abstract

Simultaneous Localization and Mapping (SLAM) is a pivotal technology at the intersection of robotics, computer vision, and autonomous systems. This comprehensive review paper explores the dynamic landscape of SLAM techniques and their multifaceted applications. Beginning with an illuminating background on the fundamental principles of SLAM, we traverse the rich tapestry of advancements in SLAM methodologies. The paper categorizes SLAM techniques into distinct paradigms, including feature-based SLAM, pose-graph SLAM, visual SLAM, and sensor fusion, elucidating their underlying algorithms, strengths, and limitations. From the intricate interplay of sensors and data sources, SLAM has matured into a versatile toolset, facilitating a broad spectrum of applications. Our exploration extends to the diverse domains benefiting from SLAM, including robotics and autonomous navigation, augmented reality, autonomous vehicles, indoor mapping, agriculture, healthcare, and more. Each application domain is examined, emphasizing the specific challenges encountered and the innovative solutions that have arisen.