Comparative Analysis of Simultaneous Localization and Mapping Algorithms for Enhanced Autonomous Navigation

This paper presents a comprehensive analysis and comparative study of the methods and approaches employed in Simultaneous Localization and Mapping (SLAM). The goal is to shed light on the advantages, disadvantages, and trade-offs of various SLAM approaches. Numerous topics are covered in the study, such as different types of sensors, data processing algorithms, feature extraction techniques, and mapping frameworks. Examining their performance in terms of accuracy, resilience, computational efficiency, and adaptability for various settings and applications is given particular priority. This work intends to help researchers and practitioners in choosing the most suitable strategies for their particular SLAM requirements by examining the benefits and drawbacks of each strategy. Additionally, it identifies important issues and potential avenues for future study to promote SLAM technological developments. The thorough analysis and comparison done here help to clarify the current status of SLAM technology and make it easier to create better localization and mapping solutions.