PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Efficient non-odometry method for environment mapping and localisation of mobile robots

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the simple algorithm of simultaneous localisation and mapping (SLAM) without odometry information. The proposed algorithm is based only on scanning laser range finder. The theoretical foundations of the proposed method are presented. The most important element of the work is the experimental research. The research underlying the paper encompasses several tests, which were carried out to build the environment map to be navigated by the mobile robot in conjunction with the trajectory planning algorithm and obstacle avoidance.
Rocznik
Strony
24--29
Opis fizyczny
Bibliogr. 25 poz., rys., wykr.
Twórcy
  • Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Białystok, Poland
  • Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Białystok, Poland
  • Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Białystok, Poland
Bibliografia
  • 1. Ambroziak L., Gosiewski Z. (2015), Two stage switching control for autonomous formation flight of unmanned aerial vehicles, Aerospace Science and Technology, Vol. 46, 221–226.
  • 2. Ambroziak L., Simha A., Pawluszewicz E., Kotta Ü., Bożko A., Kondratiuk M. (2019), Motor Failure Tolerant Control System With Self Diagnostics for Unmanned Multirotors, 24th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 422–427.
  • 3. Bekkali A., Sanson H., Matsumoto M. (2007), RFID indoor positioning based on probabilistic RFID map and Kalman filtering, Proc. of the 3rd IEEE International Conference on Wireless and Mobile Computing Networking and Communications, 21.
  • 4. Chen X, Zhang H, Lu H., Xiao J., Qiu Q., Li Y. (2017), Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue, IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), Shanghai, 41–47.
  • 5. Dellaert F. (2005): Square root SAM, Robotics Sci. Syst., ed. by S. Thrun, G. Sukhatme, S. Schaal, O. Brock, MIT Press, Cambridge.
  • 6. Dissanayake G., Durrant-Whyte H., Bailey T. (2000), A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem, Proc. of the IEEE International Conference on Robotics and Automation (ICRA) - Millennium Conference, Symposia Proceedings (Cat. No.00CH37065), Vol. 2, San Francisco, CA, USA, 1009–1014.
  • 7. Dissanayake G., Newman P., Durrant-Whyte H. F., Clark S., Csobra M. (1999), An experimental and theoretical investigation into simultaneous localisation and map building (SLAM), Proc. of the 6th International Symposium on Experimental Robotics, March, 171– 180.
  • 8. Dubbelman G., Browning B. (2015), COP-SLAM: Closed-Form Online Pose-Chain Optimization for Visual SLAM, IEEE Transactions on Robotics, Vol. 31, No. 5, Oct., 1194–1213.
  • 9. Gao M., Tang J., Yang Y., He Z., Zeng Y. (2019) An Obstacle Detection and Avoidance System for Mobile Robot with a Laser Radar, 16th IEEE International Conference on Networking, Sensing and Control (ICNSC), Banff, AB, Canada, 63–68.
  • 10. Guivant J., Nebot E. (2002), Improving computational and memory requirements of simultaneous localization and map building algorithms, Proc. of the IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Vol. 3, Washington, DC, USA, 2731–2736.
  • 11. Janah M., Fujimoto Y. (2018), Performance Analysis of an Indoor Localization and Mapping System Using 2D Laser Range Finder Sensor, 44th Annual Conference of the IEEE Industrial Electronics Society (IECON), 5463–5468.
  • 12. Klecka J., Horak K., Novacek P., Davidek D. (2016), Non-odometry SLAM and Effect of Feature Space Parametrization on its Covariance Convergence, IFAC-PapersOnLine, Vol. 49, Issue 25, 139–144.
  • 13. Kownacki, C., Ambroziak, L. (2019), Adaptation Mechanism of Asymmetrical Potential Field Improving Precision of Position Tracking in the Case of Nonholonomic UAVs, Robotica (DOI: 10.1017/S0263574719000286), Vol. 37, No.10, 1823–1834.
  • 14. Lanzon A., Freddi A., Longhi S. (2014), Flight control of a quadrotor vehicle subsequent rotor failure, Journal of Guidance Control and Dynamics, Vol. 37, No. 2, 580–591.
  • 15. Li H., Chen Q. (2010), Towards a non-probabilistic approach to hybrid geometry-topological SLAM, 8th World Congress on Intelligent Control and Automation, Jinan, 1045–1050.
  • 16. Li P., Ke Z. (2019), Feature-based SLAM for Dense Mapping, International Conference on Advanced Mechatronic Systems (ICAMechS), Kusatsu, Shiga, Japan, 372–377.
  • 17. Lu F., Milios E. (1997), Globally consistent range scan alignment for environmental mapping, Auton. Robots 4, 333–349.
  • 18. Moutarlier P., Chatila R. (1989), Stochastic multisensory data fusion for mobile robot location and environment modeling, 5th Int. Symp. Robotics Res. (ISRR), 207–216.
  • 19. Nakamura Y., Fujimoto Y. (2014), Validation of SLAM without odometry in outdoor environment, 13th IEEE International Workshop on Advanced Motion Control (AMC), Yokohama, 278–283.
  • 20. Romaniuk S., Ambroziak L., Gosiewski Z., Isto P. (2016), Real time localization system with Extended Kalman Filter for indoor applications, 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 42–47.
  • 21. Rulin H. (2017), Research on Key Technologies of Dynamic Obstacle Avoidance in Driverless Vehicles, University of Science and Technology of China.
  • 22. Smith R., Self M., Cheeseman P. (1990), Estimating uncertain spatial relationships in robotics, Autonomous Robot Vehicles, ed. by I.J. Cox, G.T. Wilfong, Springer Verlag, Berlin, Heidelberg, 167–193.
  • 23. Soragna A., Baldini M., Joho D., Kümmerle R., Grisetti G. (2019), Active SLAM using Connectivity Graphs as Priors, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 340–346.
  • 24. Xiaolin W., Jing Y., Fengchi S., Huan C., Shuzi H. (2012), An approach to multi-robot cooperative SLAM, Proc. of the 31st Chinese Control Conference, Hefei, 4904–4909.
  • 25. Zhu D., Sun X., Wang L., Liu B., Ji K. (2019), Mobile Robot SLAM Algorithm Based on Improved Firefly Particle Filter, International Conference on Robots & Intelligent System (ICRIS), Haikou, China, 35–38.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-89cd379b-243b-4b13-8ad8-5471476035bc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.