Abstract
The majority of visual SLAM techniques utilize interest points as landmarks. Therefore, they suffer from two main problems; scalability and data association reliability. Recently, there has been increasing interest in using higher level object description to reduce the number of tracked features and improve the data association among frames. In this paper, a simple visual mono SLAM algorithm is presented utilizing objects as landmarks and uses fast template matching to track predefined templates of these objects in an indoor environment. The results are described for real experiments with an indoor mobile robot platform. The performance of the proposed technique is evaluated and compared to recent methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Durrant-Whyte, H., Bailey, T.: Simultaneous Localization and Mapping: Part I. IEEE Robotics and Automation Magazine 13(2), 99–110 (2006)
Davison, A.: SLAM with a Single Camera. CML Workshop at ICRA (2002)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Davison, A., Reid, I., Molton, N., Stasse, O.: MonoSLAM: Real-Time Single Camera SLAM. IEEE Transactions on Pattern Analysis and Machine intelligence 29(6), 1052–1067 (2007)
Castle, R., Klein, G., Murray, D.: Combining monoSLAM with object recognition for scene augmentation using a wearable camera. Journal of Image and Vision Computing 28(11), 1548–1556 (2010)
Jeong, W., Lee, K.: Visual SLAM with Line and Corner Features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2570–2575 (2006)
Davison, A.: Real-Time Simultaneous Localization and Mapping with a single camera. In: IEEE International Conference onComputer Vision, vol. 2, pp. 1403–1410 (2003)
Lee, Y., Song, J.: Autonomous selection, registration, and recognition of objects for visual SLAM in indoor environments. In: ICCAS International Conference on Control, Automation and Systems, pp. 668–673 (2007)
Civera, J., Davison, A., Montiel, J.: Inverse Depth Parametrization for Monocular SLAM. IEEE Transactions on Robotics 24(5), 932–945 (2008)
Civera, J., Grasa, Ó., Davison, A., Montiel, J.: 1-Point RANSAC for EKF Filtering: Application to Real-Time Structure from Motion and Visual Odometry. Journal of Field Robotics 27(5), 609–631 (2010)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006)
Lewis, J.: Fast TemplateMatching. In: Vision Interface 1995, Canadian Image Processing and Pattern Recognition Society, pp. 120–123 (1995)
Kroon, D.: http://www.mathworks.com/matlabcentral/fileexchange/24925-fastrobust-template-matching (accessed at September 1, 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hasan, M., Abdellatif, M. (2012). Fast Template Matching of Objects for Visual SLAM. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-33503-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33502-0
Online ISBN: 978-3-642-33503-7
eBook Packages: Computer ScienceComputer Science (R0)