ROS ve Gazebo Kullanılarak Geometrik Cisimlerin SLAM Performansına Etkisinin İncelenmesi

Abstract

Robotların uygulama alanını genişletmek amacıyla bilinmeyen ortamlarda da çalışabilen otonom mobil robot geliştirme çalışmaları devam etmektedir. Otonom bir mobil robotun gezinme için bir çevre haritasına ve haritaya göre konum bilgilerine ihtiyacı vardır. Eş Zamanlı Konum Belirleme ve Haritalama (SLAM), bir otonom mobil robotun, tutarlı bir harita oluştururken konumunu belirlemek için bu haritayı kullanabileceği bir tahmin sürecidir. Bu çalışmanın amacı, geometrik nesnelerin SLAM performansı üzerindeki etkisini incelemektir. Bu doğrultuda Gazebo ortamında eşkenar üçgen prizma, kare prizma ve silindir içeren üç farklı deney alanı tasarlanmıştır. Dördüncü deney alanı, çalışmada kullanılan üç geometrik nesnenin tümünü içermektedir. SLAM algoritması, TurtleBot3 Waffle Pi robotu kullanılarak test edilmiştir. Dört deney alanının haritalama süreleri karşılaştırıldığında, en hızlı üçgen prizma ve en yavaş karma deney alanının haritasının oluşturulduğu görülmüştür. Haritada yapılan ölçümlerde gerçek ölçülere en yakın haritanın üçgen harita olduğu görülmüştür. Elde edilen bulgular, cisimlerin geometrik şekillerinin SLAM performansını direkt olarak etkilediğini göstermektedir.

Keywords

• SLAM
• Gmapping
• ROS
• Gazebo.

Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

Abstract

Development effort on Autonomous mobile robots that can operate in unknown environments are ongoing in order to extend the application area of the robots. In order to navigate, an autonomous mobile robot needs a map of the environment and location information relative to the map. Simultaneous Localization and Mapping (SLAM) is a prediction process in which the autonomous mobile robot can use this map to determine its position while building a consistent map. The purpose of this study is to examine the effect of geometric objects on SLAM performance. In this direction, three different experimental areas including equilateral triangular prisms, square prisms and cylinders are designed in a Gazebo. The fourth experiment area includes all three geometric objects used in the study. SLAM algorithm was tested by using the TurtleBot3 Waffle Pi robot. When the mapping times of the four experimental areas were compared, it was seen that the fastest scenario is achieved within triangular-only objects and the slowest within mixed prism. In terms of measures, the map including the triangular prisms is the closest to the actual measures of the simulated area. The obtained results show that the shapes of the geometric objects directly affect the performance of SLAM.

Keywords

• SLAM
• Gmapping
• ROS
• Gazebo.

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