Abstract
This paper introduces a new approach for detecting free space and obstacles in omnidirectional images that contributes to a purely vision based robot navigation in indoor environments. Naive Bayes classifiers fuse multiple visual cues and features generated from heterogeneous segmentation schemes that maintain separate appearance models and seeds for floor and obstacles regions. Pixel-wise classifications are aggregated across regions of homogeneous appearance to obtain a segmentation that is robust with respect to noise and outliers. The final classification utilizes fuzzy preference structures that interpret the individual classification as fuzzy preference relations which distinguish the uncertainty inherent to the classification in terms of conflict and ignorance. Ground truth data for training and testing the classifiers is obtained from the superposition of 3D scans captured by a photonic mixer device camera. The results demonstrate that the classification error is substantially reduced by rejecting those queries associated with a strong degree of conflict and ignorance.
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
Batavia, P., Singh, S.: Obstacle detection using adaptive color segmentation and color stereo homography. In: IEEE Int. Conf. on Robotics and Automation (2001)
Blas, M.R., Agrawal, M., Sundaresan, A., Konolige, K.: Fast color/texture segmentation for outdoor robots. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4078–4085 (2008)
Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: A survey. Journal of Intelligent Robotics System 53(3), 263–296 (2008)
Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., Bradski, G.: Self-supervised monocular road detection in desert terrain. In: Proc. of the Robotics Science and Systems Conference (2006)
Driankov, D., Saffiotti, A.: Fuzzy Logic Techniques for Autonomous Vehicle Navigation. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2001)
Fazl-Ersi, E., Tsotsos, J.K.: Region Classification for Robust Floor Detection in Indoor Environments. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 717–726. Springer, Heidelberg (2009)
Grudic, G., Mulligan, J., Otte, M., Bates, A.: Online learning of multiple perceptual models for navigation in unknown terrain. In: 6th International Conference on Field and Service Robotics (2007)
Hüllermeier, E., Brinker, K.: Learning valued preference structures for solving classification problems. Fuzzy Sets and Systems 159, 2337–2352 (2008)
Kim, D., Sun, J., Min, S., James, O., Rehg, M., Bobick, A.F.: Traversability classification using unsupervised on-line visual learning for outdoor robot navigation. In: Int. Conf. on Robotics and Automation, ICRA (2006)
Kim, Y., Kim, H.: Layered ground floor detection for vision-based mobile robot navigation. In: IEEE Int. Conf. on Robotics and Automation, vol. 1 (2004)
Lenser, S., Veloso, M.: Visual sonar: fast obstacle avoidance using monocular vision. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2003)
Noykov, S., Roumenin, C.: Occupancy grids building by sonar and mobile robot. Robotics and Autonomous Systems 55, 162–175 (2007)
Oriolo, G., Ulivi, G., Vendittelli, M.: Real-time map building and navigation for autonomous robots in unknown environments. IEEE Transactions on Systems, Man and Cybernetics Part B 28, 316–333 (1998)
Pears, N., Liang, B.: Ground plane segmentation for mobile robot visual navigation. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2001)
Plagemann, C., Endres, F., Hess, J., Stachniss, C., Burgard, W.: Monocular range sensing: A non-parametric learning approach. In: IEEE Int. Conf. on Robotics and Automation (May 2008)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: Definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41(1-2), 187–228 (2001)
Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)
Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: Proc. AAAI 2000, Austin, TX (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Posada, L.F., Narayanan, K.K., Hoffmann, F., Bertram, T. (2011). Detecting Free Space and Obstacles in Omnidirectional Images. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25486-4_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-25486-4_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25485-7
Online ISBN: 978-3-642-25486-4
eBook Packages: Computer ScienceComputer Science (R0)