Abstract
Estimating the position of a robot is an essential requirement for autonomous mobile robots. Visual Odometry is a promising localization method in slippery natural terrain, which drastically degrades the accuracy of Wheel Odometry, while relying neither on other infrastructure nor any prior knowledge. Visual Odometry, however, suffers from the instability of feature extraction from the untextured natural terrain. To date, a number of feature detectors have been proposed for stable feature detection. This paper compares commonly used detectors in terms of robustness, localization accuracy and computational efficiency, and points out their trade-off problems among those criteria. To solve the problem, a hybrid algorithm is proposed which dynamically switches between multiple detectors according to the texture of terrain. Validity of the algorithm is proved by the simulation using dataset at volcanic areas in Japan.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Triggs, B., McLauchalan, P., Hartley, R., Fitzgibbon, A.: Bundle adjustment – a modern synthesis. Vision Algorithms: Theory and Practice, 153–177 (2000)
Open source software for SLAM and loop-closing, http://openslam.org
Nister, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. J. of Field Robotics 23, 3–20 (2006)
Konolige, K., Agrawal, M.: Large-scale visual odometry for rough terrain. In: International Symposium on Research in Robotics (ISRR 2007), vol. 66 (2007)
Howard, A.: Real-time stereo visual odometry for autonomous ground vehicles. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2008), pp. 3946–3952. IEEE (2008)
Maimone, M., Cheng, Y., Matthies, L.: Two years of visual odometry on the mars exploration rovers. J. of Field Robotics 24(3), 169–186 (2007)
Johnson, A.E., Goldberg, S.B., Cheng, Y., Matthies, L.H.: Robust and efficient stereo feature tracking for visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA 2008), pp. 39–46 (2008)
Tamura, Y., Suzuki, M., Ishii, A., Kuroda, Y.: Visual odometry with effective feature sampling for untextured outdoor environment. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2009), pp. 3492–3497. IEEE (2009)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, UK, vol. 15, pp. 147–151 (1988)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), pp. 593–600. IEEE (1994)
Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision (ICCV 2005), vol. 2, pp. 1508–1515. IEEE (2005)
Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded-up robust features. Computer Vision and Image Understanding 110(3), 346–359 (2008)
Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: Center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Otsu, K., Otsuki, M., Ishigami, G., Kubota, T. (2013). An Examination of Feature Detection for Real-Time Visual Odometry in Untextured Natural Terrain. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-37374-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37373-2
Online ISBN: 978-3-642-37374-9
eBook Packages: EngineeringEngineering (R0)