Bir Mikro İHA Üzerine Entegre Edilmiş Düşük Maliyetli Mesafe Sensörleri ile İki Boyutlu Labirent Haritalandırma

Abstract

Bu çalışmada, düşük maliyetli VL53L1X mesafe sensörleri kullanılarak, bir mikro İHA (Crazyflie 2.0) yardımıyla iki boyutlu bir labirent ortamında haritalandırma yapılmıştır. Webots 2025a simülasyon ortamında yürütülen deneylerde, İHA'ya Multi-Ranger ve Flow Deck sensörleri entegre edilmiş, böylece çevresel algılama ve yön kontrolü sağlanmıştır. İHA, bir teleoperatör tarafından yönlendirilmiş ve dört yönden toplanan mesafe verileriyle temel bir 2D harita oluşturulmuştur. Sensör verilerindeki gürültüyü azaltmak için klasik Kalman filtresine göre daha dayanıklı olan Maximum Correntropy Kalman Filtresi (MCKF) uygulanmıştır. MCKF, Gauss dışı gürültü koşullarında yüksek doğruluk sağlayarak, sensör verilerinin güvenilirliğini artırmıştır. Sonuçlar, filtresiz verilerle oluşturulan dağınık haritaların aksine, filtreli verilerle daha net ve ayrıntılı haritalar elde edildiğini göstermiştir. Bu çalışma, düşük maliyetli ve hafif sensörlerin, uygun filtreleme yöntemleriyle birleştirildiğinde mikro İHA’larda güvenilir çevresel haritalandırma sağlayabileceğini ortaya koymuştur. Elde edilen bulgular, özellikle kapalı alanlarda kullanılacak düşük bütçeli otonom sistemlerin geliştirilmesi için önemli bir referans teşkil etmektedir.

Keywords

• İnsansız Hava Araçları
• Mesafe (ToF) Sensörü
• Eşzamanlı Pozisyonlama ve Haritalandırma
• Maximum Korrentropi Kalman Filtresi (MCKF)

Two-Dimensional Maze Mapping with Low-Cost Distance Sensors Integrated on a Micro UAV

Abstract

This study presents a 2D mapping approach using low-cost VL53L1X distance sensors integrated into a micro UAV (Crazyflie 2.0) in a simulated maze environment. Conducted in the Webots 2024a platform, the UAV was equipped with Multi-Ranger and Flow Deck sensors for environmental perception and control. The drone was teleoperated, and distance measurements were collected from four directions to construct a basic 2D map. To improve data reliability, the Maximum Correntropy Kalman Filter (MCKF) was applied, offering enhanced performance under non-Gaussian noise conditions compared to the classical Kalman Filter. MCKF improved state estimation accuracy and filtered out noise, resulting in more precise and detailed maps. In contrast to the noisy raw data outputs, filtered maps accurately displayed wall structures with spatial resolution up to 20 mm. The study demonstrates that when paired with effective filtering techniques, low-cost and lightweight sensors can successfully enable reliable environmental mapping in micro aerial vehicles. These findings provide a valuable foundation for the development of affordable autonomous systems, particularly for indoor applications.

Keywords

• Unmanned Aerial Vehicles
• Time-of-Flight Sensor (ToF)
• Simultaneous Localization and Mapping (SLAM)
• Maximum Correntropy Kalman filter (MCKF)

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