Enhanced Localization of ArUco Markers for Autonomous Robotics: A Comparative Study

Autonomous drone technology increasingly enables their use in diverse applications, offering cost and time benefits in precision agriculture and surveillance. They are especially efficient in search and rescue and exploring hard-to-access areas. Navigating indoor settings and partially known environments poses significant challenges in autonomous robotics. This paper introduces a novel method that leverages depth image data to substantially improve performance in these contexts. We elucidate the method's design, showcasing its dependability and advantages over conventional approaches. Furthermore, the paper delineates the critical procedures for effective autonomous robot guidance, tackling complex obstacles inherent to the field.