ArUco İşaretleyicileri Kullanarak Mobil Robotlar İçin Çoklu Kamera Tabanli İç Mekan Konum Tespiti ve Yol Optimizasyonu

Abstract

Öz: GPS sinyallerinin bulunmadığı iç mekanlarda, mobil robot konumlarını belirlemek zordur. Yaygın yöntemler arasında SLAM, işaretleyici tabanlı yerelleştirme, eylemsiz ölçüm birimleri (IMU'lar) ve hibrit konumlandırma sistemleri (HPS) bulunur. SLAM eş zamanlı yerelleştirme ve haritalama sağlarken, işaretleyici tabanlı yerelleştirme konum tespiti için belirli işaretleyiciler kullanır. IMU'lar hareketi hız, ivme ve açısal hız yoluyla izler ve HPS, gelişmiş doğruluk için sensörleri birleştirir. Bu çalışma, Cordoba Üniversitesi'nden ArUco işaretleyicileri kullanarak bir rota planlama ve hareket optimizasyon yöntemi geliştirmektedir. Görüntü işleme yoluyla tespit edilen bu işaretleyiciler, robotları hedeflere en kısa ve en güvenli yolları hesaplayarak yönlendirir. Birden fazla kamera hareket aralığını ve görüşü iyileştirirken, otomatik bir pan ayarlaması çakışmaları ve hizalama sorunlarını ele alarak sorunsuz görüntü entegrasyonunu sağlar. Önerilen yöntem, çok kameralı sistemlerin güvenilir iç mekan navigasyonu için potansiyelini göstererek endüstriyel ve hizmet alanlarında umut verici uygulamalar sunar.

Keywords

• Kapalı Alan Yer Belirleme
• Mobil Robotlar
• ArUco Markerları
• Hareket Optimizasyonu
• Çoklu Kamera Sistemleri.

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.

Keywords

• Indoor Localization
• Mobile Robots
• ArUco Markers
• Motion Optimization
• Multi-Camera Systems.

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