Computer Vision Algorithms for Autonomous Navigation

Abstract

Autonomous navigation systems, particularly those employing computer vision algorithms, have revolutionized various sectors, including agriculture, robotics, and transportation. This paper delves into the pivotal role of computer vision in enabling autonomous navigation, emphasizing its applications in GPS-denied environments. We explore key algorithms such as Simultaneous Localization and Mapping (SLAM), feature-based matching, and deep learning techniques, highlighting their integration into real-time navigation systems. The study also addresses the challenges faced in implementing these algorithms in dynamic and uncertain environments, proposing solutions to enhance robustness and efficiency. Through a review of recent advancements and case studies, this paper provides a comprehensive overview of the state-of-the-art in computer vision-based autonomous navigation