Abstract
Most visual Simultaneous Localization And Mapping (SLAM) methods use interest points as landmarks in their maps of the environment. Often the interest points are detected using contrast features, for instance those of the Scale Invariant Feature Transform (SIFT). The SIFT interest points, however, have problems with stability, and noise robustness. Taking our inspiration from human vision, we therefore propose the use of local symmetry to select interest points. Our method, the MUlti-scale Symmetry Transform (MUST), was tested on a robot-generated database including ground-truth information to quantify SLAM performance. We show that interest points selected using symmetry are more robust to noise and contrast manipulations, have a slightly better repeatability, and above all, result in better overall SLAM performance.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics, Massachusetts. The MIT Press, Cambridge (2005)
Frintrop, S., Jensfelt, P.: Attentional landmarks and active gaze control for visual slam. IEEE Transactions on Robotics 24(5) (2008)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1–16 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Se, S., Lowe, D.G., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research 21(8), 735–758 (2002)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Murillo, A.C., Guerrero, J.J., Sagues, C.: Surf features for efficient robot localization with omnidirectional images. In: IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy, pp. 3901–3907 (2007)
Davison, A.J., Murray, D.: Simultaneous localization and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 865–880 (2002)
Mozos, Ó.M., Gil, A., Ballesta, M., Reinoso, O.: Interest point detectors for visual SLAM. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds.) CAEPIA 2007. LNCS (LNAI), vol. 4788, pp. 170–179. Springer, Heidelberg (2008)
Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision 73(3), 263–284 (2007)
Palmer, S.E., Hemenway, K.: Orientation and symmetry: Effects of multiple, rotational, and near symmetries. Journal of Experimental Psychology: Human Perception and Performance 4(4), 691–702 (1978)
Kootstra, G., Nederveen, A., de Boer, B.: Paying attention to symmetry. In: Everingham, M., Needham, C., Fraile, R. (eds.) British Machine Vision Conference (BMVC 2008), Leeds, UK, pp. 1115–1125 (2008)
Kootstra, G., Schomaker, L.R.: Prediction of human eye fixations using symmetry. In: Cognitive Science Conference (CogSci), Amsterdam, NL (2009)
Driver, J., Baylis, G.C., Rafal, R.D.: Preserved figure-ground segregation and symmetry perception in visual neglect. Nature 360, 73–75 (1992)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Marola, G.: Using symmetry for detecting and locating objects in a picture. Computer Vision, Graphics, and Image Processing 46, 179–195 (1989)
Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(12), 1415–1429 (2001)
Sela, G., Levine, M.D.: Real-time attention for robotic vision. Real-Time Imaging 3, 173–194 (1997)
Heidemann, G.: Focus-of-attention from local color symmetries. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(7), 817–830 (2004)
Reisfeld, D., Wolfson, H., Yeshurun, Y.: Context free attentional operators: The generalized symmetry transform. International Journal of Computer Vision 14, 119–130 (1995)
Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 959–973 (2003)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: Part I. Simultaneous localization and mapping: Part I. 13(2), 99–108 (2006)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1/2), 43–72 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kootstra, G., de Jong, S., Schomaker, L.R.B. (2009). Using Local Symmetry for Landmark Selection. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-04667-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04666-7
Online ISBN: 978-3-642-04667-4
eBook Packages: Computer ScienceComputer Science (R0)
Keywords
- Extend Kalman Filter
- Interest Point
- Local Symmetry
- Scale Invariant Feature Transform
- Detect Interest Point
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.