2B LIDAR ile Yaya ve Mobil Robot Algılama

Öz

Robotikte aşılması gereken ilk sorun, robotun ve etrafındaki nesnelerin konumlandırılmasıdır. Robotun etrafındaki hareketli nesnelerin algılanması ve konumlandırılması, kazaları önlemek için önemli bir noktadır. Derin öğrenme ve 3D LIDAR teknolojisi, özellikle yaya tespitinde sıklıkla kullanılır. Bu çalışmalar yüksek performansa sahip olmalarına rağmen yüksek maliyetleri nedeniyle henüz yaygın olarak kullanılmamaktadır. Bu yazıda, daha düşük maliyetli 2D LIDAR'larda kullanılmak üzere bir robot ve insan algılama sistemi önerilmiştir. Sistem, sürgülü pencere (sliding window) ile 2D LIDAR ışını tarayarak robot ve insan ışın modellerini algılar. Sürgülü pencere tekniği sayesinde taradığı kısımda robot mu insan mı olduğunu işaretler. Bu çalışmada, bir simülasyon ortamında toplanan 2D LIDAR verilerine dayanan yaya ve mobil robot tanıma için yeni bir uçtan uca derin sinir ağı mimarisi önerilmiştir. Sistemin robot ve insan modellerini statik ortamda% 91,6 doğrulukla algıladığı görülmüştür.

Anahtar Kelimeler

• LIDAR
• Yaya Algılama
• Mobil Robot Algılama
• Konumlandırma
• Makine Öğrenmesi

Pedestrian and Mobile Robot Detection with 2D LIDAR

Abstract

The first problem to be overcome in robotics is the positioning of the robot and surrounding objects. Detection and positioning of moving objects around the robot are an important point to prevent accidents. Deep learning and 3D LIDAR technology are often used, especially in pedestrian detection. Although these studies have high performance, they are not widely used yet due to their high cost. In this paper, a robot and human sensing system is proposed for use in lower cost 2D LIDARs. The system detects robot and human beam patterns by scanning the 2D LIDAR beam with the sliding window. Thanks to the sliding window technique, it marks whether there is a robot or a human in the part it scans. A new end-to-end deep neural network architecture is proposed in this study for pedestrian and mobile robot recognition based on 2D LIDAR data collected in a simulation environment. It has been observed that the system perceives robot and human models in a static environment with 91.6% accuracy.

Keywords

• LIDAR
• Pedestrian Detection
• Mobile Robot Detection
• Positioning

Kaynakça

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