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Object learning and detection using evolutionary deformable models for mobile robot navigation

Published online by Cambridge University Press:  01 January 2008

M. Mata ,
J. M. Armingol ,
J. Fernández  and
A. de la Escalera
M. Mata*
Affiliation:
Computer Architecture and Automation Department, Universidad Europea de Madrid, Villaviciosa de Odon, 28670 Madrid, Spain.
J. M. Armingol
Affiliation:
Intelligent Systems Laboratory, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain. E-mail: armingol@ing.uc3m.es
J. Fernández
Affiliation:
Computer Architecture and Automation Department, Universidad Europea de Madrid, Villaviciosa de Odon, 28670 Madrid, Spain.
A. de la Escalera
Affiliation:
Intelligent Systems Laboratory, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain. E-mail: armingol@ing.uc3m.es
*
*Corresponding author. E-mail: mmata@uem.es
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Summary

Deformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. This article addresses the question of how to define a deformable model for a real-time color vision system for mobile robot navigation. Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. How well a model represents the template that exists in the image is measured by an energy function. Its minimum corresponds to the model that best fits with the image and it is found by a genetic algorithm that handles the model deformation. At a later stage, if there is symbolic information inside the object, it is extracted and interpreted using a neural network. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented in this article, showing the effectiveness and capacity of the system.

Keywords

Computer visionMatching learningDeformablemodelsLandmark navigation
Type
Article
Information
Robotica , Volume 26 , Issue 1 , January 2008 , pp. 99 - 107
Copyright
Copyright © Cambridge University Press 2007

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References

1.Balkenius, C., “Spatial learning with perceptually grounded representations,” Robot. Auton. Syst. 25, 165–175 (1998).CrossRefGoogle Scholar
2.Bhandarkar, S. M., Koh, J. and Suk, M., “Multiscale image segmentation using a hierarchical self-organizing map,” Neurocomputing 14, 241–272 (1997).CrossRefGoogle Scholar
3.Beccari, G., Caselli, S. and Zanichelli, F., “QualiSpatial learning with perceptually grounded representations,” Robot. Auton. Syst. 25, 165–175 (1998).Google Scholar
4.Betke, M. and Makris, N., “Recognition, resolution, and complexity of objects subject to affine transformations,” Int. J. Comput. Vis. 44 (1), 5–40 (2001).CrossRefGoogle Scholar
5.Ran, B., Liu, H. X. and Martonov, W., “A Vision-Based Object Detection System for Intelligent Vehicles,” Proceedings of the SPIE—the International Society for Optical Engineering (1998) vol. 3525, pp. 326–337.Google Scholar
6.Borenstein, J. and Feng, L., “Measurement and correction of systematic odometry errors in mobile robots,” IEEE Trans. Robot. Autom. 12 (5), 869–880 (1996).CrossRefGoogle Scholar
7.Burschka, D., Geiman, J. and Hager, G., “Optimal Landmark Configuration for Vision-Based Control of Mobile Robots,” Proceedings of the International Conference on Robotics and Automation (2003) pp. 3917–3922.Google Scholar
8.Escalera, A. de la, Armingol, J. M. and Mata, M., “Traffic sign recognition and analysis for intelligent vehicles,” Image Vis. Comput. 11 (3), 247–258 (2003).CrossRefGoogle Scholar
9.Escalera, A. de la, Armingol, J. M., Pastor, J. M. and Rodríguez, F. J, “Visual sign information extraction and identification by deformable models for intelligent vehicles,” IEEE Trans. Intell. Transp. Syst. 5 (2), 57–68 (2004).CrossRefGoogle Scholar
10.De Souza, G. N. and Kak, A. C., “Vision for mobile robot navigation: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 24 (2), 237–267 (2002).CrossRefGoogle Scholar
11.Franz, M. O., Schölkopf, B., Mallot, H. A. and Bülthoff, H., “Learning view graphs for robot navigation,” Auton. Robots 5, 111–125 (1998).CrossRefGoogle Scholar
12.Lijun, Y. and Basu, A., “Integrating active face tracking with model based coding,” Pattern Recognit. Lett. 20 (6), 651–657 (1999).Google Scholar
13.Mahadevan, S. and Theocharous, G., “Rapid concept learning for mobile robots,” Mach. learn. 31, 7–27 (1998).CrossRefGoogle Scholar
14.Kervrann, C. and Heitz, F., “Statistical deformable model-based segmentation of image motion,” IEEE Trans. Image Process. 8 (4), 583–588 (1999).CrossRefGoogle ScholarPubMed
15.Kreucher, C. and Lakshmanan, S., “LANA: A lane extraction algorithm that uses frequency domain features,” IEEE Trans. Robot. Autom. 15 (2), 343–350 (1999).CrossRefGoogle Scholar
16.Marsland, S., Nehmzow, U. and Duckett, T., “Learning to select distinctive landmarks for mobile robot navigation,” Robot. Auton. Syst. 37, 241–260 (2001).CrossRefGoogle Scholar
17.Poupon, F., Mangin, J. F., Hasboun, D., Poupon, C., Magnin, I. and Frouin, V., “Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures,” Proceedings of the Medical Image Computing and Computer Assisted Intervention, First International Conference (1998) pp. 1134–1143.Google Scholar
18.Rue, H. and Husby, O. K., “Identification of partly destroyed objects using deformable templates,” Stat. Comput. 8 (3), 221–228 (1998).CrossRefGoogle Scholar
19.Sala, P., Sim, R., Shokoufandeh, A. and Dickinson, S., “Landmark selection for vision-based navigation,” IEEE Trans. Robot. 22 (2), 334–349 (2006).CrossRefGoogle Scholar
20.Se, S. and Lowe, D. G. and “Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks,” Int. J. Robot. Res. 21 (8), 735–758 (2002).CrossRefGoogle Scholar
21.Se, S., Lowe, D. G. and Little, J. J., “Vision-based global localization and mapping for mobile robots” IEEE Trans. Robot. 21 (3), 364–375 (2005).CrossRefGoogle Scholar
22.Tamimi, H. and Zell, A., “Vision Based Localization of Mobile Robots Using Kernel Approaches,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2004) pp. 1896–1901.Google Scholar
23.Valveny, E. and Marti, E., “Application of Deformable Template Matching to Symbol Recognition in Handwritten Architectural Drawings,” Proceedings of the Fifth International Conference on Document Analysis and Recognition (1999) pp. 483–486.Google Scholar
24.Yoon, K., Jang, G., Kim, S. and Kweon, I., “Color landmark based self-localization for indoor mobile robots,” J. Control Autom. Syst. Eng. 7 (9), 749–757 (2001).Google Scholar
25.Yu, Z. and Jain, A. K., “Object localization using color, texture and shape,” Pattern Recognith. 33 (4), 671–684 (2000).Google Scholar