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

Yıl 2020, Sayı: 20, 343 - 350, 31.12.2020

Pınar Oğuz Ekim

https://doi.org/10.31590/ejosat.746214

Cited By: 1

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.

Destekleyen Kurum

Tübitak

Proje Numarası

119E376

Teşekkür

This research is supported by Scientific and Technological Research Council of Turkey (TUBITAK), project number 119E376.

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

Proje Numarası

119E376

Kaynakça

• Açıkel S. and Gökçen A. (2019). Localization and point cloud based 3d mapping with autonomous robots. European journal of science and technology special issue, pp. 82-92, October 2019.
• Bar-Shalom, Y., Li, X. R., and Kirubarajan, T. (2004). Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons.
• Beck, A., Stoica, P., and Li, J. (2008). Exact and approximate solutions of source localization problems. IEEE Transactions on signal processing, 56(5), 1770-1778.
• Beşkirli, M. and Tefek M. F. (2019). Parçacık sürü optimizasyon algoritması kullanılarak optimum robot yolu planlama. European journal of science and technology special issue, pp. 201-213, October 2019.
• Bostanci, B., Tekkok, S., Soyunmez, E., and Oguz-Ekim, P. (2019), viewed 17 October 2019, < https://github.com/ieuagv>
• Bregar, K., and Mohorčič, M. (2018). Improving indoor localization using convolutional neural networks on computationally restricted devices. IEEE Access, 6, 17429-17441.
• Dellaert, F., Fox, D., Burgard, W., and Thrun, S. (1999, May). Monte carlo localization for mobile robots. In Proceedings of the 1999 IEEE International conference on robotics and automation (ICRA), 2, 1322-1328.
• Dobrev, Y., Gulden, P., and Vossiek, M. (2018). An indoor positioning system based on wireless range and angle measurements assisted by multi-modal sensor fusion for service robot applications. IEEE Access, 6, 69036-69052.
• Dudek, G., and Jenkin, M. (2010). Computational principles of mobile robotics. Cambridge University Press.
• Fox, D., Burgard, W., and Thrun, S. (1998). Active markov localization for mobile robots. Robotics and Autonomous Systems, 25(3-4), 195-207.
• Golub, G. and Van Loan, C. (1996). Matrix Computations. Johns Hopkins University Press.
• González, J., Blanco, J. L., Galindo, C., Ortiz-de-Galisteo, A., Fernández-Madrigal, J. A., Moreno, F. A., and Martinez, J. L. (2009). Mobile robot localization based on ultra-wide-band ranging: A particle filter approach. Robotics and autonomous systems, 57(5), 496-507.
• Grisetti, G., Stachniss, C., and Burgard, W. (2005, April). Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In Proceedings of the 2005 IEEE International conference on robotics and automation (ICRA), 2432-2437.
• Jetto, L., Longhi, S., and Venturini, G. (1999). Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots. IEEE Transactions on Robotics and Automation, 15(2), 219-229.
• Lee, D., Son, S., Yang, K., Park, J., and Lee, H. (2009, August). Sensor fusion localization system for outdoor mobile robot. In 2009 ICCAS-SICE, 1384-1387.
• Luo F., and Fan Z. (2014). Mobile robot localization based on particle filter. In Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014.
• Payá, L., Gil, A., and Reinoso, O. (2017). A state-of-the-art review on mapping and localization of mobile robots using omnidirectional vision sensors. Journal of Sensors, 2017.
• Pozyx creater kit Lite 2015, viewed 14 October 2019, <https://www.pozyx.io/shop/product/creator-kit-lite-67>. Robot Operating System (2009), viewed 14 October 2019, < http://www.ros.org>.
• Vlassis, N., Terwijn, B., and Krose, B. (2002). Auxiliary particle filter robot localization from high-dimensional sensor observations. In Proceedings of the 2002 IEEE International conference on robotics and automation (ICRA).
• Yılmaz Z. and Bayındır L.(2019). Simulation of lidar-based robot detection task using ros and gazebo. European journal of science and technology special issue, pp. 513-529, October 2019.
• Zhang, L., Zapata, R., and Lépinay, P. (2009, October). Self-adaptive Monte Carlo localization for mobile robots using range sensors. In 2009 IEEE International Conference on Intelligent Robots and Systems (IROS), 1541-1546.