Detecting the borders of a pool-like environment by using scanning sonar data for AUVs

Abstract

This study presents a novel, cost-effective method for real-time underwater vehicle (AUV) localization in enclosed environments, pools or marinas. Traditional underwater localization techniques, often based on acoustic systems, prove costly and impractical for confined spaces. This study focuses on identifying environmental boundaries and, utilizing this information, solving the AUV's localization problem by leveraging data acquired from a 360-degree field-of-view sonar scanning sensor mounted on the AUV. Raw sonar data is processed and subsequently clustered using the K-means algorithm, enabling the identification of environmental features such as edges and corners. These identified features are then matched against a pre-existing environment map to determine the AUV's instantaneous position. Experimental results demonstrate the accuracy and reliability of the proposed approach, with small error values in corner point estimations. While the method's low computational complexity makes it suitable for real-time applications, the absence of complex and high-cost equipment requirements offers a significant advantage for daily applications. This study suggests that the proposed method has a broad application potential in AUV navigation through its iterative use.

Keywords

• Autonomous Underwater Vehicle
• Underwater Localization
• Scanning Sonar
• Data Processing
• Edge Detection

İnsansız su altı araçları için tarama sonarı verilerini kullanarak havuz benzeri ortamların kenarlarını tespit etme

Öz

Bu çalışma, havuzlar veya marinalar gibi kapalı ortamlarda gerçek zamanlı insansız su altı aracı (İSAA) konumlandırması için yeni ve uygun maliyetli bir yöntem sunmaktadır. Genellikle akustik sistemlere dayanan geleneksel su altı konumlandırma teknikleri, sınırlı alanlar için maliyetli ve pratik olmadığı kanıtlanmıştır. Bu araştırma, İSAA üzerine monte edilmiş 360 derece görüş alanına sahip bir sonar tarama sensöründen elde edilen verileri kullanarak ortam sınırlarını tespit etmeyi ve bu bilgileri kullanarak İSAA konumlandırma problemini çözmeyi hedeflemektedir. Önerilen yöntem ile ham sonar verileri işlenerek kenarlar ve köşeler gibi çevresel özelliklerin belirlenmesini sağlamak üzere K-means algoritması kullanılarak gruplanır. Belirlenen bu özellikler daha sonra İSAA'nın anlık konumunu belirlemek için bilinen ortam haritasıyla eşleştirilir. Deneysel sonuçlar, köşe noktası kestirimlerindeki düşük hata değerleri ile önerilen yaklaşımın doğruluğunu ve güvenilirliğini göstermektedir. Yöntemin düşük hesaplama karmaşıklığı gerçek zamanlı uygulamalar için uygun olmasını sağlarken, karmaşık ve yüksek maliyetli ekipman gereksinimi olmaması günlük uygulamalar için önemli bir avantaj sunmaktadır. Bu çalışma, önerilen yöntemin yinelemeli kullanımı ile İSAA navigasyonunda geniş bir uygulama potansiyeline sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Otonom İnsansız Su altı Aracı
• Su altı Konumlandırma
• Tarama Sonarı
• Veri İşleme
• Kenar Tanıma

Kaynakça

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