Abstract
When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.
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Arulampalam, S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50, 174–188.
Barreto, J., & Araujo, H. (2002). Geometric properties of central catadioptric line images. In 7th European conference on computer vision, ECCV’02 (pp. 237–251). Copenhagen, Denmark.
Bascle, B., Bouthemy, P., Deriche, R., & Meyer, F. (1994). Tracking complex primitives in an image sequence. In 12th international conference on pattern recognition (pp. 426–431).
Benosman, R., & Kang, S. (2000). Panoramic vision. New York: Springer. ISBN 0-387-95111-3.
Blake, A., Curwen, R., & Zisserman, A. A. (1993). A framework for spatiotemporal control in the tracking of visual contours. International Journal of Computer Vision, 11, 127–145.
Chen, J., Dixon, W. E., Dawson, D. M., & McIntire, M. (2003). Homography-based visual servo tracking control of a wheeled mobile robot. In International conference on intelligent robots and systems (pp. 1814–1819). Las Vegas, Nevada.
DeSouza, G. N., & Kak, A. C. (2002). Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2), 237–267.
Faugeras, O., & Lustman, F. (1988). Motion and structure from motion in a piecewise planar environment. International Journal of Pattern Recognition and Artificial Intelligence, 2(3), 485–508.
Geyer, C., & Daniilidis, K. (2000). A unifying theory for central panoramic systems and practical implications. In European conference on computer vision (Vol. 29(3), pp. 159–179). Dublin, Ireland.
Geyer, C., & Daniilidis, K. (2003). Mirrors in motion: Epipolar geometry and motion estimation. In International conference on computer vision, ICCV03 (pp. 766–773). Nice, France.
Hayet, J. B., Lerasle, F., & Devy, M. (2002). A visual landmark framework for indoor mobile robot navigation. In International conference on robotics and automation (ICRA’02) (pp. 3942–3947). Washington DC, USA.
Isard, M., & Blake, A. (1998). Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision, 29, 5–28.
Jones, S. D., Andersen, C., & Crowley, J. L. (1997). Appearance based processes for visual navigation. In IEEE/RSJ international conference on intelligent robots and systems (Vol. 2, pp. 551–557). Grenoble, France.
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contours. International Journal of Computer Vision, 1, 321–331.
Longuet-Higgins, H. C. (1981). A computer algorithm for reconstructing a scene from two projections. Nature, 293, 133–135.
López-Nicolás, G., Sagüés, C., Guerrero, J. J., Kragic, D., & Jensfelt, P. (2006). Nonholonomic epipolar visual servoing. In International conference on robotics and automation (ICRA’06) (pp. 2378–2384).
Luong, Q.-T., & Faugeras, O. (1996). The fundamental matrix: theory, algorithms, and stability analysis. International Journal of Computer Vision, 17(1), 43–76.
Ma, Y., Kosecka, J., & Sastry, S. S. (1999). Vision guided navigation for a nonholonomic mobile robot. IEEE Transactions on Robotics and Automation, 15(3), 521–337.
Malis, E., & Chaumette, F. (2000). 2 1/2 d visual servoing with respect to unknown objects through a new estimation scheme of camera displacement. International Journal of Computer Vision, 37(1), 79–97.
Malis, E., Chaumette, F., & Boudet, S. (1999). 2 1/2 d visual servoing. IEEE Transactions on Robotics and Automation, 15(2), 238–250.
Matsumoto, Y., Inaba, M., & Inoue, H. (1996). Visual navigation using view-sequenced route representation. In Proc. of the IEEE international conference on robotics and automation (Vol. 1, pp. 83–88). Minneapolis, Minnesota.
Matsumoto, Y., Ikeda, K., Inaba, M., & Inoue, H. (1999). Visual navigation using omnidirectional view sequence. In Int. conf. on intelligent robots and systems (pp. 317–322).
Nierobisch, T., Krettek, J., Khan, U., & Hoffmann, F. (2007). Optimal large view visual servoing with sets of SIFT features. In IEEE international conference on robotics and automation, ICRA’07 (pp. 2092–2097).
Remazeilles, A., Chaumette, F., & Gros, P. (2004). Robot motion control from a visual memory. In IEEE int. conf. on robotics and automation, ICRA’04 (Vol. 4, pp. 4695–4700). New Orleans.
Royer, E., Lhullier, M., Dhome, M., & Chateau, T. (2004). Towards an alternative GPS sensor in dense urban environment from visual memory. In British machine vision conference (Vol. 1, pp. 197–206). Kingston, England.
Samson, C. (1995). Control of chained systems. application to path following and time-varying stabilization of mobile robots. IEEE Transactions on Automatic Control, 40(1), 64–77.
Svoboda, T., Pajdla, T., & Hlavac, V. (1998). Motion estimation using central panoramic cameras. In IEEE conference on intelligent vehicles (pp. 335–340). Stuttgart, Germany.
Tsakiris, D., Rives, P., & Samson, C. (1998). Extending visual servoing techniques to nonholonomic mobile robots. In G. Hager, D. Kriegman, & A. Morse (Eds.), LNCIS : Vol. 237. The confluence of vision and control (pp. 106–117). Berlin: Springer.
Zhong, Y., Jain, A. K., & Dubuisson, M. P. (2000). Object tracking using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 544–549.
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Courbon, J., Mezouar, Y. & Martinet, P. Indoor navigation of a non-holonomic mobile robot using a visual memory. Auton Robot 25, 253–266 (2008). https://doi.org/10.1007/s10514-008-9093-8
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DOI: https://doi.org/10.1007/s10514-008-9093-8