Abstract
This paper presents a visual localization method based on multi-feature combination (D-\(\lambda \)LBP++HOG) using stereo images. We integrate disparity information into complete local binary features (\(\lambda \)LBP) to form a robust global image description (D-\(\lambda \)LBP). In order to represent the scene in depth, multi-feature fusion of D-\(\lambda \)LBP and HOG features, provides valuable information and permits to decrease the effect of some typical problems in place recognition such as perceptual aliasing. It improves visual recognition performance by taking the advantage of depth, texture and shape information. In addition, for real-time visual localization, local sensitive hashing method (LSH) is used to compress the high dimensional multi-feature into binary vectors. It can thus speed up the process of image matching. To show its effectiveness, the proposed method is tested and evaluated using real datasets acquired in outdoor environments. Given the obtained results, our approach allows more effective visual localization compared with the state-of-the-art method FAB-MAP.
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References
Arroyo, R., Alcantarilla, P.F., Bergasa, L., Yebes, J., Bronte, S.: Fast and effective visual place recognition using binary codes and disparity information. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3089–3094 (2014)
Arroyo, R., Alcantarilla, P., Bergasa, L., Yebes, J., Gamez, S.: Bidirectional loop closure detection on panoramas for visual navigation. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 1378–1383, June 2014
Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27(6), 647–665 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, June 2005
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)
Garcia-Fidalgo, E., Ortiz, A.: Vision-based topological mapping and localization methods: a survey. Robot. Auton. Syst. 64, 1–20 (2015)
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 58–69. Springer, Heidelberg (2006). https://doi.org/10.1007/11949619_6
Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)
Milford, M., Wyeth, G.: Seqslam: visual route-based navigation for sunny summer days and stormy winter nights. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1643–1649 (2012)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Silva, C., Bouwmans, T., Frlicot, C.: An extended center-symmetric local binary pattern for background modeling and subtraction in videos. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015), pp. 395–402, INSTICC. ScitePress (2015)
Xue, G., Song, L., Sun, J., Wu, M.: Hybrid center-symmetric local pattern for dynamic background subtraction. In: IEEE International Conference on Multimedia and Expo, pp. 1–6, July 2011
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Qiao, Y., Cappelle, C., Yang, T., Ruichek, Y. (2017). Visual Localization Based on Place Recognition Using Multi-feature Combination (D-\(\lambda \)LBP++HOG). In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_24
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DOI: https://doi.org/10.1007/978-3-319-70353-4_24
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