Article

Article title DETECTING GEOMETRIC AND SEMANTIC OBJECTS IN RANGE IMAGE FOR ROBOT NAVIGATION AND ENVIRONMENT RECONSTRUCTION
Authors V.N. Kazmin, V.P. Noskov
Section SECTION II. VISION SYSTEMS
Month, Year 10, 2015 @en
Index UDC 007:621.865.8
DOI
Abstract Solutions for problems of robot localization and constructing a map of an unknown environment, which are central in autonomous robotics, based on detecting geometric and semantic objects in range-finder data images obtained with on-board sensors while moving in industrial and urban environments, are proposed and discussed. The urgency of creation and usage of autonomous robotic systems of various purposes for such environments argued, due to limits of the usage of traditional means of navigation and remote control. The analysis of various algorithms for detecting geometric and semantic objects in range image is performed. It is shown that algorithms that take into account the structured nature of original sensor’s data are most effective. The mathematical apparatus for solution of problems of robot localization and constructing a model of the environment through the detecting and recognition of linear geometric and semantic objects in a sequence of range images obtained with on-board sensors in motion is provided. The results of the work of created algorithms, software and hardware, solving the task in the rate of motion of the mobile robot in the real world, are shown. Based on the analysis of theoretical and experimental studies it concluded that the proposed approach provides a transition from large amounts of original range-finder data to a compact semantic description of an environment and can effectively solve the problem of autonomous motion control of mobile robots and unmanned aerial vehicles.

Download PDF

Keywords Autonomous mobile robot; vision system; navigation; environment reconstruction; motion control.
References 1. Lapshov V.S., Noskov V.P., Rubtsov I.V., Rudianov N.A., Ryabov A.V., Khrushchev V.S. Boy v gorode. Boevye i obespechivayushchie roboty v usloviyakh urbanizirovannoy territorii [The battle in the city. Combat and support robots in a urban area], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2011, No. 3 (116), pp. 142-146.
2. Kalyaev A.V., Noskov V.P., Chernukhin Yu.V., Kalyaev I.A. Odnorodnye upravlyayushchie struktury adaptivnykh robotov [Homogeneous control structures of adaptive robots]. Moscow: Nauka, 1990, 147 p.
3. Noskov V.P., Rubtsov I.V. Opyt resheniya zadachi avtonomnogo upravleniya dvizheniem mobil'nykh robotov [The experience of solving the problem of Autonomous motion control of mobile robots], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, Automation, Control], 2005, No. 12, pp. 21-24.
4. Lakota N.A., Noskov V.P., Rubtsov I.V., Lundgren Ya.-O. Mur F. Opyt ispol'zovaniya elementov iskusstvennogo intellekta v sisteme upravleniya tsekhovogo transportnogo robota [Experience in the use of artificial intelligence elements in control system workshop transport robot], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, Automation, Control], 2000, No. 4, pp. 44-47.
5. Noskov V.P., Noskov A.V. Sistema ekstremal'noy navigatsii tsekhovogo transportnogo robota [Extremal navigation system of transport robot], Sb. nauchn. tr. «Iskusstvennyy intellekt v tekhnicheskikh sistemakh» [Proc. of the SIPTP “Artificial Intelligence in Engineering Systems”]. Moscow: Gos. IFTP, 1998, pp. 136-144.
6. Noskov A.V., Noskov V.P. Raspoznavanie orientirov v dal'nometricheskikh izobrazheniyakh [Features recognition in range-finder images], Sbornik «Mobil'nye roboty i mekhatronnye sistemy» [Proc. of MSU “Mobile robots and mechatronic systems”]. Moscow: Iz-vo MGU, 2001, pp. 179-192.
7. Noskov V.P., Noskov A.V. Navigatsiya mobil'nykh robotov po dal'nometricheskim
izobrazheniyam [Robots navigation based on range-finder images], Mekhatronika,
avtomatizatsiya, upravlenie [Mechatronics, Automation, Control], 2005, No. 12, pp. 16-21.
8. Noskov A.V., Rubtsov I.V., Romanov A.Yu. Formirovanie ob"edinennoy modeli vneshney sredy na osnove informatsii videokamery i dal'nomera [Formation of a unified model of the environment on the basis of information from video camera and rangefinder], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, Automation, Control], 2007, No. 8, pp. 2-5.
9. Kaz'min V.N., Noskov V.P. Ob"emnoe zrenie v sisteme navigatsionnogo obespecheniya bespilotnogo letatel'nogo apparata [Surround vision in the navigation support system unmanned aerial vehicle], Vestnik MGTU im. N.E. Baumana. Ser. Mashinostroenie [Proc. of Bauman MSTU “Machine building. Special robotics”], 2012, pp. 113-121.
10. Zagoruyko S.N., Kaz'min V.N., Noskov V.P. Navigatsiya BPLA i 3D-rekonstruktsiya vneshney sredy po dannym bortovoy STZ [3D vision-based unmanned vehicle navigation and 3D reconstruction of environments], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, Automation, Control], 2014, No. 8, pp. 62-68.
11. Segal A., Haehnel D., Thrun S. Generalized-ICP, Proc. of Robotics: Science and Systems, RSS, 2009.
12. Mitra N., Gelfand N., Pottmann H., Guibas L.J. Registration of Point Cloud Data from a Geometric Optimization Perspective, Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, 2004, pp. 22-31.
13. Fischler M.A., Bolles R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 1981, No. 24 (6), pp. 381-395.
14. Konouchine A., Gaganov V., Veznevets V. AMLESAC: A New Maximum Likelihood Robust Estimator, Graphicon 2005 proceedings. Available at: http://www.graphicon.ru/html/2005/proceedings/papers/konouchin.pdf.
15. Klasing K., Althoff D., Wollherr D., and Buss M. Comparison of Surface Normal Estimation Methods for Range Sensing Applications, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, May 12-17 2009.
16. Holz D., Holzer S., Rusu R. B., Behnke S. Real-Time Plane Segmentation using RGB-D Cameras, In Proceedings of the 15th RoboCup International Symposium, Istanbul, July 2011.
17. Comaniciu D., Meer P. Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence – PAMI, 2002, Vol. 24, No. 5, pp. 603-619.
18. Berkmann J., Caelli T. Computation of Surface Geometry and Segmentation Using Covariance Techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), November 1994, No. 16 (11), pp. 1114-1116,
19. Niloy J. Mitra and An Nguyen. Estimating surface normals in noisy point cloud data, In Proc. of The 19th ACM Symposium on Computational Geometry (SCG). San Diego, California, USA, June 2003, pp. 322-328.
20. Velizhev A., Shapovalov R., Potapov D., Tret'yak E., Konushin A. Avtomaticheskaya segmentatsiya oblakov tochek na osnove elementov poverkhnosti [Robust LIDAR data segmentation using compact point clusters], Cbornik «Trudy konfe-rentsii GraphiCon–2009» [Proc. of “GraphiCon-2009”], 2009, pp. 241-245.

Comments are closed.