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Paper Count: 30843
Virtual 3D Environments for Image-Based Navigation Algorithms
Abstract:This paper applies to the creation of virtual 3D environments for the study and development of mobile robot image based navigation algorithms and techniques, which need to operate robustly and efficiently. The test of these algorithms can be performed in a physical way, from conducting experiments on a prototype, or by numerical simulations. Current simulation platforms for robotic applications do not have flexible and updated models for image rendering, being unable to reproduce complex light effects and materials. Thus, it is necessary to create a test platform that integrates sophisticated simulated applications of real environments for navigation, with data and image processing. This work proposes the development of a high-level platform for building 3D model’s environments and the test of image-based navigation algorithms for mobile robots. Techniques were used for applying texture and lighting effects in order to accurately represent the generation of rendered images regarding the real world version. The application will integrate image processing scripts, trajectory control, dynamic modeling and simulation techniques for physics representation and picture rendering with the open source 3D creation suite - Blender.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130747Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 637
 D. Jonassen. “O uso de novas tecnologias na educação a distância e a aprendizagem construtivista” (The use of new technologies in distance education and constructive learning). In open, Brasília, 2016, n.70, apr/jun, 1996. pp 88.
 Gazebosim. Gazebo. http://www.gazebosim.org/. Retrieved October 15, 2015.
 Cyberbotics. Webots. http://www.cyberbotics.com/. Retrieved October 15, 2015.
 Coppelia. V-rep. http://www.coppeliarobotics.com/. Retrieved October 18, 2015.
 Blender 2.65a. blender.org. October 26, 2016. Retrieved October 26, 2015.
 M. Sharifi, X. Chen and C. G. Pretty, "Experimental study on using visual odometry for navigation in outdoor GPS-denied environments," 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Auckland, 2016, pp. 1-5.
 Y. Liu et al., "Stereo Visual-Inertial Odometry With Multiple Kalman Filters Ensemble," in IEEE Transactions on Industrial Electronics, vol. 63, no. 10, pp. 6205-6216, Oct. 2016.
 De La Cruz, C.; Carelli, R. “Dynamic modeling and centralized formation control of mobile robots”. In: 32nd IEEE Conference on Industrial Electronics. (S.l.: s.n.), 2006. p. 3880–3885.
 Zhang, Y. et al. “Dynamic model based robust tracking control of a differentially steered wheeled mobile robot”. American Control Conference, v. 2, 1998.
 Martins, F. N.; Carelli, R.; Sarcinelli-Filho, M.; Bastos-Filho, T. F. “Dynamic Modeling and Adaptive Dynamic Compensation for Unicycle-Like Mobile Robots”. 14th International Conference on Advanced Robotics - ICAR 2009, Germany, June, 22-26, 2009.
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