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

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 konumunuhesaplamak için kullanılır. Daha sonra bu başlangıç pozisyonunundan yararlanarak, ilk yönelim açısını bulmak için Işık Algılama veUzaklık (LiDAR) sensörünün taramaları ve tarama eşleştirme tekniği önerilmiştir. Böylece, robotik uygulamalarda önemli bir sorunolan başlangıçtaki otonom konumlandırma ve yönelim açısını bulma çözüldü. UWB uzaklık ölçümleri, kilometre odometre veUyarlanabilir Monte Carlo Lokalizasyon (AMCL) poz bilgisini birleştiren Genişletilmiş Kalman Filtresi, robotun yörüngesi sırasındakonumu bulmak için benimsenmiştir. Gerçek ve simülasyon ortamları için Robot İşletim Sistemleri (ROS) için yeni modülleruygulanmıştır ve geniş çapta benimsenmesini sağlamak için açık kaynak olarak yapılmıştır. Simülasyon sonuçları, önerilen yönteminKök Ortalama Kare Hatasının (RMSE) 3 cm olduğunu ve kıyaslama yöntemden neredeyse iki kat daha iyi olduğunu göstermiştir.

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

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 forcalculating the initial robot position. Then by exploiting the initial position, Light Detection and Ranging (LiDAR) scans and scanmatching technique have been proposed to find the initial heading. Thus, the autonomous pose initialization, which is an importantproblem in robotic applications, is solved. The Extended Kalman Filter, which fuses UWB range measurements, odometry andAdaptive Monte Carlo Localization (AMCL) pose information, is adopted to localize the robot during its trajectory. New moduleshave been implemented for Robot Operating Systems (ROS) for real and simulation environments and they are made to be opensource 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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