Panoramic representation for route recognition by a mobile robot

Here, we explore a new theme: Route recognition, in robot navigation. It is faced with problems of visual sensing, spatial memory construction, and scene recognition in a global world. The strategy of this work is route description from experience, that is, a robot acquires a route description from route views taken in a trial move, and then uses it to guide the navigation along the same route. In cognition phase, a new representation of scenes along a route termed panoramic representation is proposed. This representation is obtained by scanning sideviews along the route, which provides rich information such as 2D projections of scenes called Panoramic view and generalized panoramic view, a path-oriented 2 1/2D sketch, and a path description, but only contains a small amount of data. The continuous panoramic view (PV) and generalized panoramic view (GPV) are efficient in processing, compared with fusing discrete views into a complete route model. In recognition phase, the robot matches the panoramic representation memorized in the trial move and that from incoming images so that it can locate and orient itself. We employ dynamic programming and circular dynamic programming in coarse matching of GPVs and PVs, and employ feature matching in fine verification. The advantage of wide fields of GPV and PV brings a reliable result to the scene recognition.

[1]  Matthew J. Barth,et al.  Qualitative route scene description using autonomous landmark detection , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[2]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[3]  M D'Zmura,et al.  Mechanisms of color constancy. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[4]  G. Cohen.  Memory In The Real World , 1989 .

[5]  Saburo Tsuji,et al.  Panoramic representation of scenes for route understanding , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[6]  Larry S. Davis,et al.  A visual navigation system for autonomous land vehicles , 1987, IEEE J. Robotics Autom..

[7]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[8]  Martial Hebert,et al.  Vision and navigation for the Carnegie-Mellon Navlab , 1988 .

[9]  Takeo Kanade,et al.  Stereo by Two-Level Dynamic Programming , 1985, IJCAI.

[10]  William B. Thompson,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2009 .

[11]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[12]  David Marr,et al.  Representing Visual Information , 1977 .

[13]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[14]  Matthew Turk,et al.  VITS-A Vision System for Autonomous Land Vehicle Navigation , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Robert C. Bolles,et al.  Epipolar-plane image analysis: An approach to determining structure from motion , 1987, International Journal of Computer Vision.

[16]  鄭 絳字.  Dynamic Projection Panoramic Representation and Route Recognition , 1990 .

[17]  Tod S. Levitt,et al.  Terrain models for an autonomous land vehicle , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[18]  Minoru Asada,et al.  Color-based panoramic representation of outdoor environment for a mobile robot , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[19]  Olivier D. Faugeras,et al.  Building, Registrating, and Fusing Noisy Visual Maps , 1988, Int. J. Robotics Res..

[20]  E. Land.  Recent advances in retinex theory , 1986, Vision Research.

[21]  E. D. Dickmanns,et al.  A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision , 1987, Other Conferences.

[22]  Saburo Tsuji,et al.  From anorthoscope perception to dynamic vision , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[23]  David J. Heeger,et al.  Optical flow from spatialtemporal filters , 1987 .

[24]  Robert C. Bolles,et al.  Generalizing Epipolar-Plane Image Analysis on the spatiotemporal surface , 2004, International Journal of Computer Vision.