Email this article Characterizing Indoor Vanishing Points by Local Dominant Orientation Signature from Omnidirectional Vision
Abstract
For a mobile robot, it is necessary to have the ability to move accurately and autonomously in an known and/or unknown environment. The vanishing points existing in indoor environment are a key factor for the success of robot localization and navigation. To successfully detect these vanishing points, this paper proposes a novel approach of utilizing the distribution of orientation information from omnidirectional vision. The local orientation data are first computed by proper edge detection and then characterized by the local dominant orientation signature descriptors. These descriptors are further filtered based on the strength of its own orientation as well as its neighboring orientations. The final orientation field is constructed by using the interpolation of radial basis functions. The proposed approach of vanishing point detection has been extensively tested in various indoor environments such as narrow pathway library and multi-path corridor. Experimental results demonstrate good performance compared with existing approach.
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H. Huttenrauch, A. Green, M. Norman, L. Oestreicher, and K. Severinson Eklundh, “Involving Users in the Design of a Mobile Office Robot,” IEEE Transactions on Systems, Man, and Cybernetics - part C: Applications and Reviews, Vol. 34, No. 2, pp. 113-124, May. 2004.
E. Menegatti, A. Pretto, A. Scarpa, and E. Pagello, “Omnidirectional Vision Scan Matching for Robot Localization in Dynamic Environments,” IEEE Transactions on Robotics, Vol. 22, No. 3, pp. 523-535, Jun. 2006. CrossRef
C. Silpa-Anan and R. Hartley, "Visual localization and loop-back detection with a high resolution omnidirectional camera," The 6thWorkshop on Omnidirectional Vision, Camera Networks and Non-classical cameras, Oct. 21, 2005.
Z. Ma and H. Kim, “Heading Direction Computation of Golf-Ball Collecting Robot Using Vanishing Points,” in Proceedings of the IEEE International Conference on Information and Automation, Shenzhen, China, pp. 792-796, Jun. 6-8, 2011.
R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense tracking and mapping in real-time,” in Proceedings of the International Conference on Computer Vision (ICCV), 2011. CrossRef
F. Zheng, R. Schubert, and G. Welch, “A General Approach for Closed-Loop Registration in AR,” in Proceedings of the IEEE Conference on Virtual Reality, Orlando, FL, USA, pp. 47-50, Mar. 16-20, 2013. CrossRef
J. Engel, T. Schops, and D. Cremers, "LSD-SLAM: Large-Scale Direct Monocular SLAM," European Conference on Computer Vision, Zurich, Switzerland, pp. 1-16, Sep. 6-12, 2014. CrossRef
J. Sturm, E. Bylow, F. Kahl, and D. Cremers, “CopyMe3D: Scanning and printing persons in 3D,” in Pattern Recognition, Editor: J. Weicker, M. Hein, and B. Schiele, Berlin Heidelberg: Springer, pp. 405-414, 2013.
Y. Gao, J. Luo, H. Qiu, and B. Wu, "Survey of structure from motion," in Proceedins of International Conference on Cloud Computing and Internet of Things, Changchun, China, pp. 72-76, Dec. 13-14, 2014. CrossRef
N. Snavely, S. M. Seitz, and R. Szeliski, “Photo Tourism: Exploring Photo Collections in 3D,” ACM Transactions on Graphics, Vol. 25, No. 3, pp. 835-846, Jul. 2006. CrossRef
J. Heller, M. Havlena, A. Sugimoto, and T. Pajdla, ”Structure-from-Motion Based Hand-Eye Calibration using
M.-D. Yang, C.-F. Chao, K.-S. Huang, L.-Y. Lu, and Y.-P. Chen, "Image-based 3D scene reconstruction and exploration in augmented reality," Automation in Construction, vol. 33, pp. 48-60, Aug. 2013. CrossRef
D. Turne, A. Lucieer, and C. Watson, "An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds," Remote Sensing, vol. 4, no. 5, pp. 1392-1410, May 2012. CrossRef
J. Choi, W. Kim, H. Kong, and C. Kim, "Real-time vanishing point detection using the Local Dominant Orientation Signature," 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, Antalya, Turkey, May 16-18, 2011.
J. A. Gaspar, “Omnidirectional Vision for Mobile Robot Navigation,” PhD thesis, Technical Superior Institute, Technical University of Lisbon, Dec. 2002.
R. Ebrahimpour, R. Rasoolinezhad, Z. Hajiabolhasani, and M. Ebrahimi, “Vanishing Point Detection in Corridors: Using Hough Transform and K-means Clustering,” IET Computer Vision, Vol. 6, No. 1, pp. 40-51, Jan. 2012. CrossRef
L. F. Posada, K. K. Narayanan, F. Hoffmann and T. Bertram, “Floor Segmentation of Omnidirectional Images for Mobile Robot Visual Navigation,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 804-809, Oct. 18-22, 2010. CrossRef
S. Kim, S.-Y. Oh, “SLAM in Indoor Environments Using Omni-directional Vertical and Horizontal Line Features,” Journal of Intelligent and Robotic Systems, Vol. 51, No. 1, pp. 31-43, Jan. 2008. CrossRef
J. M. Coughlan and A. L. Yuille, “Manhattan World: Orientation and Outlier Detection by Bayesian Inference,” Neural Computation, Vol. 15, No. 5, pp. 1063-1088, May 2003. CrossRef
A. T. Martins, P. M. Q. Aguiar, and M. A. T. Figueiredo, “Orientation in Manhattan: Equiprojective Classes and Sequential Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No.5, pp. 822-827, May 2005. CrossRef
S. T. Barnard, “Interpreting Perspective Image,” Artificial Intelligence Journal, Vol. 21, No. 4, pp. 435-462, Nov. 1983. CrossRef
J. Kim, M. Hwangbo, and T. Kanade, “Spherical Approximation for Multiple Cameras in Motion Estimation: Its Applicability and Advantages,” Computer Vision and Image Understanding, Vol. 114, No. 10, pp. 1068-1083, Oct. 2010. CrossRef
J. P. Barreto and H. Araujo, “Geometric Properties of Central Catadioptric Line Images and Their Application in Calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1327-1333, Aug. 2005. CrossRef
M. E. Antone and S. Teller, “Automatic Recovery of Relative Camera Rotations for Urban Scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, USA, Vol. 2, pp. 282-289, Jun. 13-15, 2000. CrossRef
M. Bosse, R. Rikoski, J. Leonard, S. Teller, “Vanishing Points and 3D Lines From Omnidirectional Video,” in Proceedings of the IEEE International Conference on Image Processing, Rochester, New York, Vol.3, pp. 513- 516, Jun. 24-28, 2002. CrossRef
J.-C. Bazin, C. Demonceaux, P. Vasseur, and I. Kweon, “Rotation Estimation and Vanishing Point Extraction by Omnidirectional Vision in Urban Environment,” the International Journal of Robotics Research, Vol. 31, No. 1, pp. 63-81, Jan. 2012.
S.-B. Kim, J.-C. Bazin, H.-K. Lee, K.-H. Choi, S.-Y. Park, “Ground Vehicle Navigation in Harsh Urban Conditions by Integrating Inertial Navigation System, Global Positioning System, Odometer and Vision Data,” IET Radar, Sonar and Navigation, Vol. 5, No. 8, pp. 814–823, Oct. 2011. CrossRef
L . Quan and R. Mohr, “Determining Perspective Structures Using Hierarchical Hough Transform,” Pattern Recognition Letters, Vol. 9, No. 4, pp. 279-286, May 1989. CrossRef
J. A. Shufelt, “Performance Evaluation and Analysis of Vanishing Point Detection Techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 3, pp. 282-288, Mar. 1999. CrossRef
J.-C. Bazin, P.-Y. Laffont, I. Kweon, C. Demonceaux, and P. Vasseur, “An Original Approach for Automatic Plane Extraction by Omnidirectional Vision,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 752-758, Oct. 18-22, 2010. CrossRef
A. Banno and K. Ikeuchi, “Omnidirectional Texturing Based on Robust 3D Registration through Euclidean Reconstruction from Two Spherical Images,” Computer Vision and Image Understanding, Vol. 114, No. 4, pp. 491-499, Apr. 2010. CrossRef
B. Bonev, M. Cazorla and F. Escolano, “Robot Navigation Behaviors Based on Omnidirectional Vision and Information Theory,” Journal of Physical Agents, Vol. 1, No. 1, pp. 27-36, July 2007.
N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, Vol. 1, pp. 886–893, Jun. 2005. CrossRef
J. Barillari. (2005, May 22). Merida, Mexico. Wikipedia, [Online]. VIEW ITEM
(2012, Apr. 27). Radial basis function. Wikipedia, [Online]. VIEW ITEM
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