Visual Simultaneous Localization and Mapping (VSLAM) has emerged as a critical technology in the realm of autonomous vehicles, facilitating real-time navigation and mapping in complex and dynamic environments. This research paper provides a comprehensive analysis of the fundamental principles, implementation strategies, performance evaluation, and potential applications of VSLAM for autonomous vehicles. The paper begins by elucidating the foundational components of VSLAM, including camera calibration, feature extraction, feature tracking, and camera pose estimation. It then delves into the practical implementation of VSLAM within autonomous vehicles, highlighting the integration of advanced algorithms, sensor fusion techniques, and high-performance computational infrastructure to enable robust navigation and mapping capabilities. Performance evaluation and benchmarking methodologies for VSLAM are extensively discussed, encompassing a range of metrics for assessing accuracy, robustness, and computational efficiency. The comparative analysis of different VSLAM approaches provides valuable insights into their respective strengths and limitations in autonomous vehicle navigation and mapping scenarios. Challenges and future directions in the field of VSLAM are identified, emphasizing the need to address perceptual ambiguity, enhance realtime processing capabilities, ensure long-term mapping stability, and integrate semantic understanding for improved scene interpretation. The diverse applications of VSLAM, spanning urban navigation, infrastructure inspection, logistics management, and disaster response, underscore its transformative impact on transportation and mobility.
