A Virtual Simulation of the Image Based Self-navigation of Mobile Robots
The paper concerns with the problem of fully visual self-navigation of mobile robots based on the analysis of similarity of images, acquired by the cameras mounted on the robot, with some previously captured images stored in a database. In order to simplify and speed-up the extraction of the necessary data from the image database it is assumed that the rough position of the robot is known e.g. based on the GPS module or some other sensors. Due to the application of the image analysis methods, the accuracy of the self-positioning of the robot can be significantly improved leading to fully visual self-navigation of autonomous mobile robots, assuming their continuous access to the image database. In order to verify the validity of the proposed approach, the virtual simulation environment based on the Simbad 3D robot simulator has been prepared. The initial results presented in the paper, obtained for synthetic images captured by the virtual robots, confirm the usefulness of the proposed approach being a good starting point for future experiments using the real images captured by the physical mobile robot also in various lighting conditions.
Keywordsmachine vision visual robot navigation mobile robots
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- 4.Domek, S., Dworak, P., Grudziński, M., Okarma, K.: Calibration of cameras and fringe pattern projectors in the vision system for positioning of workpieces on the CNC machines. In: Gosiewski, Z., Kulesza, Z. (eds.) Mechatronic Systems and Materials V, Solid State Phenomena, vol. 199, pp. 229–234. Trans Tech Publications (2013)Google Scholar
- 5.Garcia, R., Nicosevici, T., Ridao, P., Ribas, D.: Towards a real-time vision-based navigation system for a small-class UUV. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 1, pp. 818–823 (2003)Google Scholar
- 6.Hugues, L., Bredeche, N.: Simbad: An autonomous robot simulation package for education and research. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 831–842. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 7.Li, C., Bovik, A.: Three-component weighted structural similarity index. In: Proceedings of SPIE - Image Quality and System Performance VI, San Jose, California, vol. 7242, p. 72420Q (2009)Google Scholar
- 10.Okarma, K., Tecław, M., Lech, P.: Application of super-resolution algorithms for the navigation of autonomous mobile robots. In: Choraś, R.S. (ed.) Image Processing & Communications Challenges 6. AISC, vol. 313, pp. 147–154. Springer, Heidelberg (2015)Google Scholar
- 12.Žujović, J., Pappas, T.N., Neuhoff, D.L.: Structural similarity metrics for texture analysis and retrieval. In: Proc. 16 th IEEE Int. Conf. Image Processing ICIP, pp. 2225–2228 (2009)Google Scholar
- 14.Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for image quality assessment. In: Proc. 37th IEEE Asilomar Conf. Signals, Systems and Computers, Pacific Grove, California (2003)Google Scholar