Görsel Odometride SIFT, SURF, FAST, STAR ve ORB özellik algılama algoritmalarının Performans ve Takas Değerlendirmesi

Abstract

Son yıllarda görsel odometri alanında, robotik ve otomotiv teknolojisinde görsel tabanlı ölçüm gibi pratik süreçlerin geliştirilmesine yol açan çok sayıda araştırma ve çalışma yapılmıştır. Doğrudan yöntemler, özellik tabanlı yöntemler ve hibrit yöntemler, görsel odometri problemlerinin çözümünde üç yaygın yaklaşımdır ve öznitelik tabanlı yaklaşım hızlarının daha yüksek olduğu genel inancı göz önüne alındığında, bu yaklaşım son yıllarda memnuniyetle karşılanmaktadır. Bu nedenle, bu çalışmada, kamera dönüşü ve çevirisindeki değişiklikleri tahmin edebilen değişmez özellikler kullanılarak iki boyutlu sıralı görüntü setlerinin dönüşüm matrisini hesaplamak için bir girişimde bulunulmuştur. Algoritmada, anahtar noktaların belirlenmesi ve aykırı değerlerin kaldırılması için iki adım, sırasıyla beş farklı yerel özellik algılama algoritması (SURF, SIFT, FAST, STAR, ORB) ve RANdom SAmple Consensus algoritması (RANSAC) kullanılarak gerçekleştirilir. Ek olarak, her birinin etkisi, içsel parametreleri ve dinamik gürültü, dönüşüm matrisinin doğruluğu üzerinde değerlendirilir ve rotasyonel MSE ve hesaplama çalışma süresi açısından analiz edilir.

Keywords

• Görsel Odometri
• Görüntü İşleme
• Değişmeyen Özellikler
• Yerel Özellik Algılama
• Anahtar Noktalar
• RANSAC
• Dönüşüm Matrisi

Performance and Trade-off Evaluation of SIFT, SURF, FAST, STAR and ORB feature detection algorithms in Visual Odometry

Abstract

In recent years there has been a great deal of research and study in the field of visual odometry, which has led to the development of practical processes such as visual based measurement in robotics and automotive technology. Direct methods, feature-based methods and hybrid methods are three common approaches in solving visual odometry problems and given the general belief that feature-based approach speeds are higher, this approach has been welcomed in recent years. Therefore, an attempt has been made in the present study to calculate the transformation matrix of two-dimensional sequential image sets using invariant features that can estimate the changes in camera rotation and translation. In the algorithm, two-steps of identifying keypoints and removing outliers are performed using five different local feature detection algorithms (SURF, SIFT, FAST, STAR, ORB) and RANdom SAmple Consensus algorithm (RANSAC), respectively. In addition, the impact of each of them, their intrinsic parameters and dynamic noise on the accuracy of the transformation matrix are evaluated and analyzed in terms of rotational MSE and computational runtime.

Keywords

• Visual Odometry
• Image Processing
• Invariant Features
• Local Feature Detection
• Keypoints
• RANSAC
• Transformation Matrix

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