Yıl: 2019 Cilt: 27 Sayı: 5 Sayfa Aralığı: 3980 - 3993 Metin Dili: İngilizce DOI: 10.3906/elk-1807-335 İndeks Tarihi: 20-05-2020

A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Öz:
Map merging is a noteworthy phenomenon for cases such as search and rescue and disaster areas in whichthe duration is quite significant when gathering information about an environment. It is obvious that the total mappingtime decreases if the number of agents (robots) increases. However, the use of multiple agents leads to problems suchas task allocation schemes and the fusing of local maps. Examining the present methods, it is generally observed thatthe common features of local maps have been found and the global map is formed by obtaining related transformationbetween local maps. However, such implementations may be risky when local maps have symmetrical areas. Hence, anovel and semantic approach has been developed to solve this problem. The developed method counts on the reliabilitylevel of feature points. If relevant feature points are trusted, local maps are merged according to the best point orpoints. The simulation results from a robot operating system and a real-time experiment support the proposed method’sefficiency, and mapping can be performed even for environments that have symmetrical similar parts and the task timecan thus be reduced.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik Bilgisayar Bilimleri, Yazılım Mühendisliği Bilgisayar Bilimleri, Sibernitik Bilgisayar Bilimleri, Bilgi Sistemleri Bilgisayar Bilimleri, Donanım ve Mimari Bilgisayar Bilimleri, Teori ve Metotlar Bilgisayar Bilimleri, Yapay Zeka
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Riazuelo L, Tenorth M, Marco DD, Salas M, Gálvez-López D et al. Roboearth semantic mapping: a cloud enabled knowledge-based approach. IEEE Transactions on Automation Science and Engineering 2015; 12 (2): 432-443. doi: 10.1109/TASE.2014.2377791
  • [2] Wallgrün IJ. Hierarchical Voronoi Graphs: Spatial Representation and Reasoning for Mobile Robots. Berlin, Germany: Springer Heidelberg, 2010.
  • [3] Khan S, Ahmmed MK. Where am I? Autonomous navigation system of a mobile robot in an unknown environment. In: 2016 5th International Conference on Informatics, Electronics and Vision; Dhaka, Bangladesh; 2016. pp. 56-61.
  • [4] Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine 2006; 13 (2): 99-108. doi: 10.1109/MRA.2006.1638022
  • [5] Quang HP, Quoc NL. Some improvements in the RGB-D SLAM system. In: 2015 IEEE RIVF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for the Future; Can Tho, Vietnam; 2015. pp. 112-116.
  • [6] Yoo JK, Kim JH. Gaze control-based navigation architecture with a situation-specific preference approach for humanoid robots. IEEE/ASME Transactions on Mechatronics 2015; 20 (5): 2425-2436. doi: 10.1109/TMECH.2014.2382633
  • [7] Thrun S, Montemerlo M, Koller D, Wegbreit B, Nieto J et al. Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association. Journal of Machine Learning Research 2004; 4 (3): 380-407.
  • [8] De Silva O, Mann GK, Gosine RG. An ultrasonic and vision-based relative positioning sensor for multirobot localization. IEEE Sensors Journal 2015; 15 (3): 1716-1726. doi: 10.1109/JSEN.2014.2364684
  • [9] Schmidt A. Multi-robot, ekf-based visual slam system. In: Computer Vision and Graphics, ICCVG 2014; Cham, Switzerland; 2014.
  • [10] Topal S, Erkmen I, Erkmen AM. A novel multirobot map fusion strategy for occupancy grid maps. Turkish Journal of Electrical Engineering and Computer Sciences 2013; 21 (1): 107-119. doi: 10.3906/elk-1106-18
  • [11] Huang WH, Beevers KR. Topological map merging. International Journal of Robotics Research 2005; 24 (8): 601- 613.
  • [12] Topal S, Erkmen I, Erkmen AM. A novel map merging methodology for multi-robot systems. In: Proceedings of the World Congress on Engineering and Computer Science; San Francisco, CA, USA; 2010. pp. 383-387.
  • [13] Birk A, Carpin S. Merging occupancy grid maps from multiple robots. Proceedings of the IEEE 2006; 94 (7): 1384-1397. doi: 10.1109/JPROC.2006.876965
  • [14] Bonanni TM, Della Corte B, Grisetti G. 3-d map merging on pose graphs. IEEE Robotics and Automation Letters 2017; 2 (2): 1031-1038. doi: 10.1109/LRA.2017.2655139
  • [15] Lee HC, Roh BS, Lee BH. Multi-hypothesis map merging with sinogram-based PSO for multi-robot systems. Electronics Letters 2016; 52 (14): 1213-1214. doi: 10.1049/el.2016.1041
  • [16] Wang K, Jia S, Li Y, Li X, Guo B. Research on map merging for multi-robotic system based on rtm. In: 2012 International Conference on Information and Automation; Shenyang, China; 2012. pp. 156-161.
  • [17] Thrun S. Robotic mapping: a survey. In: Lakemeyer G, Nebel B (editors). Exploring Artificial Intelligence in the New Millennium. San Francisco, CA, USA: Morgan Kaufmann, 2003. pp. 1-36.
  • [18] Samir S, Elouardi A, Samir B, Belhocine M. Vehicle localization systems: towards low-cost architectures. Turkish Journal of Electrical Engineering and Computer Sciences 2016; 24 (4): 2010-2027. doi: 10.3906/elk-1402-260
  • [19] Aragues R, Sagues C, Mezouar Y. Map Merging. Parallel and Distributed Map Merging and Localization: Algorithms, Tools and Strategies for Robotic Networks. New York, NY, USA: Springer International Publishing, 2015.
  • [20] Aragues R, Cortes J, Sagues C. Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Transactions on Robotics 2012; 28 (4): 840-854. doi: 10.1109/TRO.2012.2192012
  • [21] Li H, Tsukada M, Nashashibi F, Parent M. Multivehicle cooperative local mapping: A methodology based on occupancy grid map merging. IEEE Transactions on Intelligent Transportation Systems 2014; 15 (5): 2089-2100. doi: 10.1109/TITS.2014.2309639
  • [22] Aragues R, Sagues C, Mezouar Y. Feature-based map merging with dynamic consensus on information increments. Autonomous Robots 2015; 38 (3): 243-259. doi: 10.1007/s10514-014-9406-z
  • 23] Tamas L, Goron LC. 3d map building with mobile robots. In: 20th Mediterranean Conference on Control & Automation; Barcelona, Spain; 2012. pp. 134-139.
  • [24] Lee HC, Lee BH. Enhanced-spectrum-based map merging for multi-robot systems. Advanced Robotics 2013; 27 (16): 1285-1300. doi: 10.1080/01691864.2013.819609
  • [25] Tsardoulias E, Thallas A, Petrou L. Metric Map Merging using RFID Tags & Topological Information. arXiv: 1711.06591, 2017.
  • [26] Ferrão VT, Vinhal CDN, Cruz G. An occupancy grid map merging algorithm invariant to scale, rotation and translation. In: 2017 Brazilian Conference on Intelligent Systems; Uberlandia, Brazil; 2017. pp. 246-251.
  • [27] Saeedi S, Paull L, Trentini M, Li H. Occupancy grid map merging for multiple robot simultaneous localization and mapping. International Journal of Robotics and Automation 2015; 30 (2): 149-157. doi: 10.2316/Journal.206.2015.2.206-4028
  • [28] Harris C, Stephens M. A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference; Manchester, UK; 1988. pp. 147-151.
  • [29] Chong NS, Yau HK, Mou LDW. Visual detection in omnidirectional view sensors. Signal, Image and Video Processing 2015; 9 (4): 923-940. doi: 10.1007/s11760-013-0528-0
  • [30] Proença H. Performance evaluation of keypoint detection and matching techniques on grayscale data. Signal, Image and Video Processing 2015; 9 (5): 1009-1019. doi: 10.1007/s11760-013-0535-1
  • [31] Shi J. Good features to track. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Seattle, WA, USA; 1994. pp. 593-600.
  • [32] Rosten E, Drummond T. Machine learning for high-speed corner detection. In: European Conference on Computer Vision; Graz, Austria; 2006. pp. 430-443.
  • [33] Bay H, Tuytelaars T, Van Gool L. SURF: Speeded up robust features. In: European Conference on Computer Vision; Graz, Austria; 2006. pp. 404-417.
  • [34] Durdu A, Korkmaz M. Autonomously simultaneous localization and mapping based on line tracking in a factory-like environment. Advances in Electrical and Electronic Engineering Journal 2019; 17 (1): 45-53. doi: 10.15598/aeee.v17i1.3048
APA Durdu A, KORKMAZ M (2019). A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. , 3980 - 3993. 10.3906/elk-1807-335
Chicago Durdu Akif,KORKMAZ MEHMET A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. (2019): 3980 - 3993. 10.3906/elk-1807-335
MLA Durdu Akif,KORKMAZ MEHMET A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. , 2019, ss.3980 - 3993. 10.3906/elk-1807-335
AMA Durdu A,KORKMAZ M A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. . 2019; 3980 - 3993. 10.3906/elk-1807-335
Vancouver Durdu A,KORKMAZ M A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. . 2019; 3980 - 3993. 10.3906/elk-1807-335
IEEE Durdu A,KORKMAZ M "A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach." , ss.3980 - 3993, 2019. 10.3906/elk-1807-335
ISNAD Durdu, Akif - KORKMAZ, MEHMET. "A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach". (2019), 3980-3993. https://doi.org/10.3906/elk-1807-335
APA Durdu A, KORKMAZ M (2019). A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. Turkish Journal of Electrical Engineering and Computer Sciences, 27(5), 3980 - 3993. 10.3906/elk-1807-335
Chicago Durdu Akif,KORKMAZ MEHMET A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. Turkish Journal of Electrical Engineering and Computer Sciences 27, no.5 (2019): 3980 - 3993. 10.3906/elk-1807-335
MLA Durdu Akif,KORKMAZ MEHMET A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. Turkish Journal of Electrical Engineering and Computer Sciences, vol.27, no.5, 2019, ss.3980 - 3993. 10.3906/elk-1807-335
AMA Durdu A,KORKMAZ M A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(5): 3980 - 3993. 10.3906/elk-1807-335
Vancouver Durdu A,KORKMAZ M A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(5): 3980 - 3993. 10.3906/elk-1807-335
IEEE Durdu A,KORKMAZ M "A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach." Turkish Journal of Electrical Engineering and Computer Sciences, 27, ss.3980 - 3993, 2019. 10.3906/elk-1807-335
ISNAD Durdu, Akif - KORKMAZ, MEHMET. "A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach". Turkish Journal of Electrical Engineering and Computer Sciences 27/5 (2019), 3980-3993. https://doi.org/10.3906/elk-1807-335