Abstract
This paper presents the marker-less augmented reality system for in-situ visualization of robot’s plans to the human operator. The system finds the natural features in the environment and builds the 3D map of the working space during the mapping phase. The stereo from motion method is utilized to compute the 3D position of natural features, while the position of the camera is computed from the artificial markers placed in the working space. Therefore the map is build in the fixed frame of reference frame provided by artificial markers. When the whole working space is mapped, artificial markers are not required for the functionality of the augmented reality system. The actually seen natural features are compared to those stored in the map and camera pose is estimated according found correspondences. The main advantages are that no artificial markers are necessary during regular use of the system, and that method does not rely on the tracking. Even the single frame is sufficient to compute the pose of camera and visualize the robot’s plan. As there is a big number of natural features in the environment, the precision of the camera pose estimation is sufficient, when the camera is looking into the mapped working space.
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References
Basu, S., Essa, I., Pentland, A.: Motion regularization for model-based head tracking. In: Proceedings of 13th International Conference on Pattern Recognition, vol. 3, pp. 611–616. IEEE (1996). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=547019
Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Proceedings of the Ninth European Conference on Computer Vision. Graz, Austria, May 2006
Bradski, G.: The openCV library. Dr Dobbs J. Softw. Tools 25, 120–125 (2000). http://opencv.willowgarage.com
Comport, A.I., Marchand, E., Pressigout, M., Chaumette, F.: Real-time markerless tracking for augmented reality: the virtual visual servoing framework. IEEE Trans. Vis. Comput. Graph. 12(4), 615–628 (2006)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007). http://dl.acm.org/citation.cfm?id=1263144.1263479
Gao, X.S., Hou, X.R., Tang, J., Cheng, H.F.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 930–943 (2003)
Hartley, R.I., Sturm, P.: Triangulation. Comput. Vis. Image Underst. 68(2), 146–157 (1997). http://linkinghub.elsevier.com/retrieve/pii/S1077314297905476
Jurie, F., Dhome, M.: A simple and efficient template matching algorithm. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 544–549. IEEE Computer Soceity (2001). http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=937673
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81(2), 155–166 (2008). http://link.springer.com/10.1007/s11263-008-0152-6
Lourakis, M.I.A., Argyros, A.A.: Efficient, causal camera tracking in unprepared environments. Comput. Vis. Image Underst. 99(2), 259–290 (2005)
Roller, D., Daniilidis, K., Nagel, H.H.: Model-based object tracking in monocular image sequences of road traffic scenes. Int. J. Comput. Vis. 10(3), 257–281 (1993). http://link.springer.com/10.1007/BF01539538
Teichrieb, V., Lima, M., Lourenc, E., Bueno, S., Kelner, J., Santos, I.H.F.: A survey of online monocular markerless augmented reality. Int. J. Model. Simul. Petrol. Ind. 1(1), 1–7 (2007). http://rpcmod.ganer.ex-br.com/revista/articles/1.pdf
Uenohara, M., Kanade, T.: Vision-based object registration for real-time image overlay. Comput. Biol. Med. 25(2), 249–260 (1995). http://www.sciencedirect.com/science/article/pii/001048259400045R
VTT Technical Research Centre of Finland: ALVAR: A Library for Virtual and Augmented Reality. http://virtual.vtt.fi/virtual/proj2/multimedia/alvar
Acknowledgments
This work has been supported by Czech Ministry for Education, Youth and Sport under mobility project number 7AMB14DE004, by German Academic Exchange Service DAAD under mobility project number 57065853 and by Czech Science Foundation under research project No. 13–30155P.
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Košnar, K., Vick, A., Přeučil, L., Krüger, J. (2015). Marker-Less Augmented Reality for Human Robot Interaction. In: Hodicky, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2015. Lecture Notes in Computer Science(), vol 9055. Springer, Cham. https://doi.org/10.1007/978-3-319-22383-4_14
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