Multicamera-Based Indoor Localization and Path Optimization for Mobile Robots Using ArUco Markers

Abstract

Abstract : In indoor environments where GPS signals are unavailable, determining mobile robot positions is challenging. Common methods include SLAM, marker-based localization, inertial measurement units (IMUs), and hybrid positioning systems (HPS). SLAM enables simultaneous localization and mapping, while marker-based localization uses specific markers for position detection. IMUs track motion via velocity, acceleration, and angular velocity, and HPS combines sensors for improved accuracy. This study develops a route planning and motion optimization method using ArUco markers from the University of Córdoba. Detected via image processing, these markers guide robots by calculating the shortest and safest paths to targets. Multiple cameras enhance motion range and vision, while an automatic pan adjustment addresses overlaps and alignment issues, ensuring seamless image integration. The proposed method demonstrates the potential of multi-camera systems for reliable indoor navigation, offering promising applications in industrial and service domains.