Multi-level exploration and 3D mapping with octrees and differential drive robots

• Autorzy: Kudriashov A. , Buratowski T. , Giergiel M.
• Języki publikacji: EN

Abstrakty

EN

In this paper the 3D SLAM solution for indoor inspection tasks with wheeled mobile robots is introduced. The application is regarded to exploring and map creating in multi-level buildings with usage of differential drive robots. Working environment is represented as three-dimensional occupancy grid map, that constructed by laser rangefinder sensor system and octrees. Robot's base frame displacement and orientation is given from visual odometry and inertial navigation system feedback. The pose estimation process is based on combined particular and Gaussian filtering techniques. The whole SLAM system is implemented in ROS framework in accordance with multi-agent extension requirements, and therefore might be used for a mobile robot group applications.

Słowa kluczowe

EN

vision systems; sensor system; 3D mapping; drive robots

PL

system wizyjny; system sensoryczny; obrazowanie 3D; napęd robota

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2018
• Tom: z. 196
• Strony: 491--500
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Kudriashov A.

• AGH University of Science and Technology, Department of Robotics and Mechatronics, Al. Mickiewicza 30, D-1 building, 30-059 Krakow

autor

Buratowski T.

• AGH University of Science and Technology, Department of Robotics and Mechatronics, Al. Mickiewicza 30, D-1 building, 30-059 Krakow

autor

Giergiel M.

• AGH University of Science and Technology, Department of Robotics and Mechatronics, Al. Mickiewicza 30, D-1 building, 30-059 Krakow

Bibliografia

• [1] T. Buratowski, M. Ciszewski, M. Giergiel, A. Kudriashov, and L. Mitka. Robot z laserowym czujnikiem odległości do budowy map 2d. Modelowanie inżynierskie, 2016, t. 30, nr. 61, s. 27-33
• [2] A. Doucet, N. De Freitas, K. Murphy, and S. Russell. Rao-blackwellised particle filtering for dynamic bayesian networks. In Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, 2000, Morgan Kaufmann Publishers Inc, s. 176-183
• [3] G. Dudek and M. Jenkin. Computational principles of mobile robotics. Cambridge University Press 2010
• [4] A. Elfes. Using occupancy grids for mobile robot perception and navigation. Computer, 1989, vol. 22, nr. 6, s. 46-57
• [5] A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard. OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 2013, vol. 34, nr 3, s. 189-206
• [6] G. Grisetti, C. Stachniss, and W. Burgard. Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE transactions on Robotics, 2007, vol. 23, nr. 1, s. 3446
• [7] M. Jaimez, J. Monroy, J. Gonzalez-Jimenez. Planar Odometry from a Radial Laser Scanner. A Range Flow-based Approach, In IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016, s. 4479-4485
• [8] A. Kudriashov, T. Buratowski, and M. Giergiel. Robots pose estimation in environment exploration process with slam and laser techniques. Naukov Notatki, Lutsk, 2017, nr 58, s. 204-212.
• [9] H. P. Moravec. Sensor fusion in certainty grids for mobile robots. AI magazine, 1988, vol. 9, nr. 2, s.61.
• [10] M. I. Ribeiro and P. Lima. Kinematics models of mobile robots. Institute Superior Tcnico, Institute de Sistemas e Robotica, Lisboa, 2002.
• [11] S. Thrun, W. Burgard, and D. Fox. Probabilistic robotics, MIT Press, Cambridge (Mass), 2005.
• [12] S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust monte carlo localization for mobile robots. Artificial intelligence, Elsevier, 2001, vol. 128, nr 1-2, s. 99-141.
• [13] K. M. Wurm, A. Hornung, M . Bennewitz, C. Stachniss, and W. Burgard. Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In Proc. of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation, 2010.
• [14] J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. In Robotics: Science and Systems Conference (RSS). July 2014, Berkeley, CA.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).