Abstract
We present a different approach of feature point detection for improving the accuracy of SLAM using single, monocular camera. Traditionally, Harris Corner detection, SURF or FAST corner detectors are used for finding feature points of interest in the image. We replace this with another approach, which involves building non-linear scale space representation of images using Perona and Malik Diffusion equation and computing the scale normalized Hessian at multiple scale levels (KAZE feature). The feature points so detected are used to estimate the state and pose of a mono camera using extended Kalman filter. By using accelerated KAZE features and a more rigorous feature rejection routine combined with 1-point RANSAC for outlier rejection, short baseline matching of features are significantly improved, even with lesser number of feature points, especially in the presence of motion blur. We present a comparative study of our proposal with FAST and show improved localization accuracy in terms of absolute trajectory error.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Albrecht, S.: An analysis of visual mono-slam. Diss. Master’s Thesis. Universität Osnabrück, 2009 (2009)
Alcantarilla, P.F., Solutions, T.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Patt. Anal. Mach. Intell. 34(7), 1281–1298 (2011)
Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 214–227. Springer, Heidelberg (2012)
Civera, J., Davison, A.J., Montiel, J.M.: Inverse depth parametrization for monocular slam. IEEE Trans. Rob. 24(5), 932–945 (2008)
Civera, J., Grasa, O.G., Davison, A.J., Montiel, J.: 1-point ransac for extended kalman filtering: Application to real-time structure from motion and visual odometry. J. Field Robot. 27(5), 609–631 (2010)
Davison, A.J., Murray, D.W.: Simultaneous localization and map-building using active vision. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 865–880 (2002)
Feng, L., Wu, Z., Long, X.: Fast image diffusion for feature detection and description. Int. J. Comput. Theory Eng. 8(1), 58–62 (2016)
Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vision 94(3), 335–360 (2011)
Hartmann, J.M., Klussendorff, J.H., Maehle, E.: A comparison of feature descriptors for visual slam. In: 2013 European Conference on Mobile Robots (ECMR), pp. 56–61. IEEE (2013)
Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2007, pp. 225–234. IEEE (2007)
Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: Dense tracking and mapping in real-time. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327. IEEE (2011)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: http://vision.in.tum.de/data/datasets/rgbd-dataset/download
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 573–580. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sengupta, A., Elanattil, S. (2015). New Feature Detection Mechanism for Extended Kalman Filter Based Monocular SLAM with 1-Point RANSAC. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-26832-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26831-6
Online ISBN: 978-3-319-26832-3
eBook Packages: Computer ScienceComputer Science (R0)