Abstract
This article presents a visual localization technique based solely on the use of omnidirectional images, within the framework of mobile robotics. The proposal makes use of the epipolar constraint, adapted to the omnidirectional reference, in order to deal with matching point detection, which ultimately determines a motion transformation for localizing the robot. The principal contributions lay on the propagation of the current uncertainty to the matching. Besides, a Bayesian regression technique is also implemented, in order te reinforce the robustness. As a result, we provide a reliable adaptive matching, which proves its stability and consistency against non-linear and dynamic effects affecting the image frame, and consequently the final application. In particular, the search for matching points is highly reduced, thus aiding in the search and avoiding false correspondes. The final outcome is reflected by real data experiments, which confirm the benefit of these contributions, and also test the suitability of the localization when it is embedded on a vSLAM application.
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 subscriptionsReferences
Bay, H., Tuytelaars, T., Van Gool, L.: Speeded up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)
Brand, C., Schuster, M.J., Hirschmller, H., Suppa, M.: Submap matching for stereo-vision based indoor/outdoor slam. In: IEEE IROS, pp. 5670–5677 (2015)
Brown, M.Z., Burschka, D., Hager, G.D.: Advances in computational stereo. IEEE Trans. PAMI 25(8), 993–1008 (2003)
Caruso, D., Engel, J., Cremers, D.: Large-scale direct slam for omni directional cameras. In: IEEE IROS, pp. 141–148 (2015)
Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: ICCV, vol. 2, pp. 1403–1410, France (2003)
Engel, J., Stuckler, J., Cremers, D.: Large-scale direct slam with stereo cameras. In: IEEE IROS, pp. 1935–1942 (2015)
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R., Andrade-Cetto, J.: Exploration on continuous Gaussian process frontier maps. In: IEEE ICRA, pp. 6077–6082, China (2014)
Gil, A., Reinoso, O., Ballesta, M., Juliá, M., Payá, L.: Estimation of visual maps with a robot network equipped with vision sensors. Sensors 10, 5209–5232 (2010)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)
Huang, S., Dissanayake, G.: Convergence and consistency analysis for extended Kalman filter based slam. IEEE Trans. Rob. 23(5), 1036–1049 (2007)
Joly, C., Rives, P.: Bearing-only SAM using a minimal inverse depth parametrization. In: ICINCO, vol. 2, pp. 281–288 (2010)
Kulback, S., Leiber, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Lee, S.J., Song, J.B.: A new sonar salient feature structure for EKF-based slam. In: IEEE IROS, pp. 5966–5971 (2010)
Leung, C., Huang, S., Dissanayake, G.: Active slam in structured environments. In: IEEE ICRA, pp. 1898–1903 (2008)
Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature 293(5828), 133–135 (1985)
Paya, L., Amoros, F., Fernandez, L., Reinoso, O.: Performance of global-appearance descriptors in map building and localization using omnidirectional vision. Sensors 14, 3033–3064 (2014)
Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. In: Adaptive Computation and Machine Learning series. Massachusetts Institute of Technology (2006)
Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omni directional cameras. In: IEEE IROS, pp. 5695–5701, China (2006)
Servos, J., Smart, M., Waslander, S.: Underwater stereo slam with refraction correction. In: IEEE IROS, pp. 3350–3355 (2013)
Shuang, Y., Baoyuan, C., Lei, Z., Xiaoyang, Y., Haibin, W., Jixun, Z., Deyun, C.: Encoded light image active feature matching approach in binocular stereo vision. In: IFOST, pp. 406–409 (2016)
Valiente, D., Gil, A., Fernandez, L., Reinoso, O.: A modified stochastic gradient descent algorithm for view-based slam using omnidirectional images. Inf. Sci. 279, 326–337 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Valiente, D., Reinoso, Ó., Gil, A., Payá, L., Ballesta, M. (2017). Omnidirectional Localization in vSLAM with Uncertainty Propagation and Bayesian Regression. 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_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-70353-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70352-7
Online ISBN: 978-3-319-70353-4
eBook Packages: Computer ScienceComputer Science (R0)