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Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2020-20-3-371-376
RESEARCH OF VISUAL SIMULTANEOUS LOCALIZATION AND MAPPING-BASED NAVIGATION SYSTEM FOR MOBILE ROBOTS
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Article in Russian
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Abstract
For citation:
Othman W., Gromov V.S. Research of visual simultaneous localization and mapping-based navigation system for mobile robots. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 3, pp. 371–376 (in Russian). doi: 10.17586/2226-1494-2020-20-3-371-376
Abstract
Subject of Research. The paper considers navigation system for mobile robots by building a map of the environment using Simultaneous Localization and Mapping algorithm and converting the 3D map collected by INTEL Realsense Depth camera into a 2D-cost map. Method. Real-Time Appearance-Based Mapping was used for building a virtual 3D map of the environment. A binary map was obtained by projecting the 3D map on a plane. The D* algorithm was applied on the binary map for planning a global path to the goal. The Dynamic-Window Approach was used as a local planner. Main Results. A point cloud of the environment was created and converted to a 2D map. A robot was safely navigated to the desired location. Practical Relevance. The proposed approach is fast and reliable and can be used for indoor navigation (factories and companies). Since the map needs to be designed only once, the calculation can be handled by CPU without any need for graphics processing unit.
Keywords: navigation, path planning, RTAB-Map, SLAM, D* algorithm
References
References
-
Meyer J.-A., Filliat D. Map-based navigation in Mobile robots: II. A review of map-learning and path-planning strategies. Cognitive Systems Research, 2003, vol. 4, no. 4, pp. 283–317. doi: 10.1016/S1389-0417(03)00007-X
-
Güzel M. Autonomous vehicle navigation using vision and mapless strategies: A survey. Advances in Mechanical Engineering, 2013, pp. 234747. doi: 10.1155/2013/234747
-
Aulinas J., Petillot Y., Salvi J., Lladó X. The SLAM problem: a survey. Frontiers in Artificial Intelligence and Applications, 2008, vol. 184, pp. 363–371. doi: 10.3233/978-1-58603-925-7-363
-
Labbé M., Michaud F. RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. Journal of Field Robotics, 2019, vol. 36, no. 2, pp. 416–466. doi: 10.1002/rob.21831
-
Mur-Artal R., Tardós J.D. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 2017, vol. 33, no. 5, pp. 1255–1262. doi: 10.1109/TRO.2017.2705103
-
Victerpaul P., Saravanan D., Janakiraman S., Pradeep J. Path planning of autonomous mobile robots: A survey and comparison. Journal of Advanced Research in Dynamical and Control Systems, 2017, vol. 9, no. 12 (spec.issue), pp. 1535–1565.
-
Liu X., Gong D. A comparative study of a-star algorithms for search and rescue in perfect maze. Proc. of the International Conference on Electric Information and Control Engineering (ICEICE 2011), 2011, pp. 24–27. doi: 10.1109/ICEICE.2011.5777723
-
Stentz A. The D* algorithm for real-time planning of optimal traverses. Technical Report, Carnegie Mellon University, Pittsburgh, PA, 1994, CMU-RI-TR-94-37.
-
Fox D., Burgard W., Thrun S. The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine, 1997, vol. 4, no. 1, pp. 23–33. doi: 10.1109/100.580977
-
Taketomi T., Uchiyama H., Ikeda S. Visual SLAM algorithms: A survey from 2010 to 2016. IPSJ Transactions on Computer Vision and Applications, 2017, vol. 9, pp. 16. doi: 10.1186/s41074-017-0027-2
-
Rublee E., Rabaud V., Konolige K., Bradski G. ORB: An efficient alternative to SIFT or SURF. Proc. of the IEEE International Conference on Computer Vision (ICCV 2011), 2011, pp. 2564–2571. doi: 10.1109/ICCV.2011.6126544
-
Hast A., Sablina V.A., Kylberg G., Sintorn I.-M. A simple and efficient feature descriptor for fast matching. Full Papers Proc. 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), University of West Bohemia, Plzen, Czech Republic, 2015, pp. 135–142.
-
Yuvaraju M., Sheela K., Sobana R. Feature extraction of real-time image using sift algorithm. International Journal of Research in Electrical and Electronics Engineering, 2015, vol. 3, no. 4, pp. 1–7.
-
Labbé M., Michaud F. Online global loop closure detection for large-scale multi-session graph-based SLAM. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 2014, pp. 2661–2666. doi: 10.1109/IROS.2014.6942926