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Improvement of speeded-up robust features for robot visual simultaneous localization and mapping

Published online by Cambridge University Press:  02 September 2013

Yin-Tien Wang  and
Guan-Yu Lin
Yin-Tien Wang*
Affiliation:
Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, New Taipei City 25137, Taiwan
Guan-Yu Lin
Affiliation:
Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, New Taipei City 25137, Taiwan
*
*Corresponding author. E-mail: [email protected]
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Summary

A robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.

Keywords

Robot mappingSpeeded-up robust features (SURF)Simultaneous localization and mapping (SLAM)Image processingRobot vision
Type
Articles
Information
Robotica , Volume 32 , Issue 4 , July 2014 , pp. 533 - 549
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Artieda, J., Sebastian, J. M., Campoy, P., Correa, J. F., Mondragón, I. F., Martínez, C. and Olivares, M., “Visual 3-d slam from uavs,” J. Intell. Robot. Syst. 55, 299–321 (2009).Google Scholar
2.Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L., “SURF: Speeded-up robust features,” Comput. Vis. Image Underst. 110, 346–359 (2008).Google Scholar
3.Cocaud, C. and Kubota, T., “SURF-Based SLAM Scheme Using Octree Occupancy Grid for Autonomous Landing on Asteroids,” Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Sapporo, Japan, (2010) pp. 275–282.Google Scholar
4.Davison, A. J., Reid, I. D., Molton, N. D. and Stasse, O., “MonoSLAM: Real time single camera SLAM,” IEEE Trans. Pattern Anal. Mach. Intell. 29 (6), 1052–1067 (2007).Google Scholar
5.Gil, A., Mozos, O. M., Ballesta, M. and Reinoso, O., “A comparative evaluation of interest point detectors and local descriptors for visual SLAM,” Mach. Vis. Appl. 21, 905–920 (2010).Google Scholar
6.Harris, C. and Stephens, M., “A Combined Corner and Edge Detector,” Proceedings of the 4th Alvey Vision Conference, University of Manchester, Manchester, UK (1988) pp. 147–151.Google Scholar
7.Karlsson, N., di Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P. and Munich, M. E., “The vSLAM Algorithm for Robust Localization and Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 24–29.Google Scholar
8.Lindeberg, T., “Feature detection with automatic scale selection,” Int. J. Comput. Vis. 30 (2), 79–116 (1998).Google Scholar
9.Lowe, D. G., “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60 (2), 91–110 (2004).CrossRefGoogle Scholar
10.Murillo, A. C., Guerrero, J. J. and Sagues, C., “SURF Features For Efficient Robot Localization with Omnidirectional Images,” Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy (2007) pp. 3901–3907.Google Scholar
11.Paz, L. M., Pinies, P., Tardos, J. D. and Neira, J., “Large-scale 6-DOF SLAM with stereo-in-hand,” IEEE Trans. Robot. 24 (5), 946–957 (2008).Google Scholar
12.Shakhnarovich, G., Darrell, T. and Indyk, P., Nearest-Neighbor Methods in Learning and Vision (The MIT Press, MA, 2005).Google Scholar
13.Viola, P. A. and Jones, M. J., “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii (2001) pp. 511–518.Google Scholar
14.Wu, C. Y. and Fu, L. C., “An Integrated Robotic vSLAM System to Realize Exploration in Large Indoor Environment,” Proceedings of the CACS International Automatic Control Conference, Taichung, Taiwan (2007).Google Scholar