ROS Ekosistemi ile Robotik Uygulamalar için UWB, LiDAR ve Odometriye Dayalı Konumlandırma ve İlklendirme Algoritmaları

Öz

Bu çalışmada, Turtlebot3 ve daha birçok mobil robot üzerindeki konum bulma sorunu ile birlikte ilklendirme sorunu açıklanmaktadır. Ultra geniş bant (UWB) sensörlerinden elde edilen uzaklıkların kareleri ölçümleri ve en küçük kareler tekniği ilk robot konumunu hesaplamak için kullanılır. Daha sonra bu başlangıç pozisyonunundan yararlanarak, ilk yönelim açısını bulmak için Işık Algılama ve Uzaklık (LiDAR) sensörünün taramalarını kullanan tarama eşleştirme tekniği önerilmiştir. Böylece, robotik uygulamalarda önemli bir sorun olan başlangıçtaki otonom konumlandırma ve yönelim açısını bulma çözülmüştür. UWB uzaklık ölçümleri, odometre ve Uyarlanabilir Monte Carlo Lokalizasyon (AMCL) poz bilgisini birleştiren Genişletilmiş Kalman Filtresi (EKF), robotun gittiği yol boyunca robotun konumunu bulmak için benimsenmiştir. Gerçek ve simülasyon ortamlarında kullanılmak üzere Robot İşletim Sistemleri (ROS) için yeni modüller uygulanmıştır ve geniş çapta benimsenmesini sağlamak için açık kaynak olarak yapılmıştır. Simülasyon sonuçları, önerilen yöntemin Kök Ortalama Kare Hatasının (RMSE) 3 cm olduğunu ve kıyaslama yöntemden neredeyse iki kat daha iyi olduğunu göstermiştir.

Anahtar Kelimeler

• Genişletilmiş kalman filtresi,, otonom mobil robotlar,robot navigasyonu,robot konumlandırması,ultra geniş band

Localization and Initialization Algorithms based on UWB, LiDAR and Odometry for Robotic Applications with ROS Ecosystem

Öz

This paper describes the initialization problem along with the localization problem over the Turtlebot3 and many more mobile robots. The least squares techniques and the squared range measurements obtained from ultra-wide band (UWB) sensors are used for calculating the initial robot position. Then by exploiting the initial position, Light Detection and Ranging (LiDAR) scans and scan matching technique have been proposed to find the initial heading. Thus, the autonomous pose initialization, which is an important problem in robotic applications, is solved. The Extended Kalman Filter, which fuses UWB range measurements, odometry and Adaptive Monte Carlo Localization (AMCL) pose information, is adopted to localize the robot during its trajectory. New modules have been implemented for Robot Operating Systems (ROS) for real and simulation environments and they are made to be open source to enable wide-spread adoption. The simulation results have shown that the proposed method’s Root Mean Square Error (RMSE) is 3 cm and it’s almost twice better in accuracy than the benchmarked method.

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