Abstract
In ubiquitous computing systems, determining the location of objects in the environment can provide basic information about the context of such objects. In closed environments an Interior Positioning System (IPS) helps to determine the location of people or robots through the use of a point-based reference system placed in the environment. Several mechanisms can be used to locate references, for example: light sensing, radio frequencies, sound, or images. In this paper, it is presented an image-based IPS that finds the location of a robot in a zone and provides functions to generate paths for the robot. The zones are identified through reference markers, which are analyzed in a server using image processing and Cloud Robotics, in order to minimize processing load in the robot. Once the marker is analyzed, a route is sent to the robot.
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References
Bonaccorsi, M., Fiorini, L., Cavallo, F., Esposito, R., Dario, P.: Design of cloud robotic services for senior citizens to improve independent living and personal health management. In: Andó, B., Siciliano, P., Marletta, V., Monteriú, A. (eds.) Ambient Assisted Living. BIOSYSROB, vol. 11, pp. 465–475. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18374-9_43
Caron, C., Chamberland-Tremblay, D., Lapierre, C., Hadaya, P., Roche, S., Saada, M.: Indoor Positioning, pp. 1011–1019. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-17885-1
Chen, Q., Petriu, E., Yang, X.: A comparative study of fourier descriptors and hu’s seven moment invariants for image recognition. In: Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513), vol. 1, pp. 103–106, May 2004
Cruz-Hernández, H., de la Fraga, L.G.: A fiducial tag invariant to rotation, translation, and perspective transformations. Pattern Recognit. 81, 213–223 (2018). http://www.sciencedirect.com/science/article/pii/S0031320318301146
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)
Focardi, R., Luccio, F.L., Wahsheh, H.A.M.: Usable cryptographic QR codes. In: 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 1664–1669, February 2018
Foundation, R.P.: What is a raspberry pi (2018). https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/
Ifukube, T., Sasaki, T., Peng, C.: A blind mobility aid modeled after echolocation of bats. IEEE Trans. Biomed. Eng. 38(5), 461–465 (1991)
La Delfa, G.C., Catania, V., Monteleone, S., De Paz, J.F., Bajo, J.: Computer vision based indoor navigation: a visual markers evaluation. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 376, pp. 165–173. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19695-4_17
Li, D. (ed.): RGB, pp. 1791–1791. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-48998-8
Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013)
Schwiegelshohn, F., Wehner, P., Werner, F., Gohringer, D., Hubner, M.: Enabling indoor object localization through bluetooth beacons on the radio robot platform. In: 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), pp. 328–333, July 2016
Shabalina, K., Sagitov, A., Li, H., Magid, E.: Comparing fiducial marker systems occlusion resilience through a robot eye. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 273–278, June 2017
Shang, W., Huang, H., Zhu, H., Lin, Y., Wang, Z., Qu, Y.: An improved kNN algorithm – fuzzy kNN. In: Hao, Y., et al. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 741–746. Springer, Heidelberg (2005). https://doi.org/10.1007/11596448_109
Sharma, O., Pandey, J., Akhtar, H., Rathee, G.: Navigation in AR based on digital replicas. Vis. Comput. 34, 925–936 (2018)
Song, Z., Jiang, G., Huang, C.: A survey on indoor positioning technologies. In: Zhou, Q. (ed.) ICTMF 2011. CCIS, vol. 164, pp. 198–206. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24999-0_28
Starek, M.J.: Light Detection and Ranging (LIDAR), pp. 383–384. Springer, Dordrecht (2016). https://doi.org/10.1007/978-94-017-8801-4
Teh, C.H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Mach. Intell. 10(4), 496–513 (1988)
Wang, Q., Li, Z., Wang, J., Liu, S., Li, D.: A fast processing method of foreign fiber images based on HSV color space. In: Li, D., Chen, Y. (eds.) CCTA 2012. IAICT, vol. 392, pp. 390–397. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36124-1_47
Weiser, M.: The computer for the 21st century. SIGMOBILE Mob. Comput. Commun. Rev. 3(3), 3–11 (1999)
Werner, M.: Basic Positioning Techniques, pp. 73–99. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10699-1_3
Youssef, M.: Indoor Localization, pp. 1004–1010. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-17885-1
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Ramírez-Díaz, A.J., Rodríguez-García, J., Mendoza, S., Viveros, A.M. (2019). Indoor Location and Tracking System Using Computer Vision. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_61
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