Abstract
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.
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References
J.Y. Zheng, Dynamic projection, panoramic representation, and route recognition, Ph.D. thesis in Osaka University, December 1989.
A.M.Waxman, J.J.LeMoigne, and B.Srinivasan, A visual navigation system for autonomous land vehicles, IEEE J. Robotics Autom. RA-3(2): 124–141, 1987.
C.Thorpe, M.H.Hebert, T.Kanade, and S.A.Shafer, Vision and navigation for the Carnegie-Mellon Navilab, IEEE Trans. Patt. Anal. Mach. Intell. 10(3): 362–373, 1988.
M.Turk, K.D. Morgenthaler, K.D. Gremban, and M. Marra, A vision system for autonomous land vehicle navigation, IEEE Trans. Patt. Anal. Mach. Intell. 10(3): 342–360.
Dickmanns and A. Zapp, A curvature-based scheme for improving road vehicle guidance by computer vision, Proc. SPIE Conf. 727 on Mobile Robot, November 1986.
A.Elfes, Sonar-based real-world mapping and navigation, IEEE J. Robot. Autom. RA-3(3): 249–265, 1987.
N. Ayache and O. Faugeras, Building, registering, and fusing noisy visual maps. Proc. Intern. Conf. Comput. Vis., pp. 73–82, 1987.
S.Tsuji and J.Y.Zheng, Visual Path Planning, Proc. 10th Intern. Joint Conf. Artif. Intell. 2: 1127–1130, Milan, 1987.
G.Cohen, Memory in the real world, Lawrence Erlbaum Associates, Hove. 1989.
D.T. Lawton et al., Terrain models for an autonomous land vehicle, Proc. IEEE Conf. Robot. Autom., pp. 2043–2051, 1986
B. Bhanu, H. Nasr, and S. Schaffer, Guiding an autonomous land vehicle using knowledge-based landmark recognition, DARPA Proc. Image Understanding Workshop, Los Angeles, pp. 432–439, 1987.
J.Y.Zheng, M.Asada, and S.Tsuji, Color-based panoramic representation of outdoor environment for a mobile robot, Proc. 9th Intern. Conf. Patt. Recog., Rome, 2: 801–803, 1988.
J.Y.Zheng and S.Tsuji, Panoramic representation of scenes for route understanding, Proc. 10th Intern. Conf. Patt. Recog., Atlantic City, 1: 161–167, 1990.
D.Marr, Representing visual information. In A.Hanson and E.M.Riseman, eds., Computer Vision Systems, Academic Press: New York, 1978.
J.Y.Zheng and S.Tsuji, Spatial representation and analysis of temporal visual events, Proc. IEEE Intern. Conf. Image Processing 2: 775–779, 1989.
J.Y.Zheng and S.Tsuji, From anorthoscopic perception to dynamic vision, Proc. IEEE Conf. Robot. Autom. 2: 1154–1160, Cincinnati, 1990.
R.C.Bolles, H.Baker, and D.H.Marimont, Epipolar-plane image analysis: An approach to determining structure from motion, Intern. J. Comput. Vis. 1(1): 7–55, June 1987.
D.J. Heeger, Optical flow from spatiotemporal filters, Proc. 1st Intern. Conf. Comput. Vis. pp. 181–190, London, 1987.
H. Baker and R.C. Bolles, Generalizing epipolar-plane image analysis on the spatiotemporal surface, Proc. Conf. Comput. Vis. Patt. Recog. pp. 2–9, Ann Arbor, 1988.
Y.Ohta and T.Kanade, Stereo by two-level dynamic programming, Proc. 9th Intern. Joint Conf. Artif. Intell. 2: 1120–1126, Los Angeles, 1985.
A.R.Bruss and B.K.P.Horn, Passive navigation, Comput. Vis. Graph. Image Process. 21(1): 3–20, 1983.
B.K.P.Horn, Determining lightness from an image, Comput. Graph. Image Process. 3: 277–299, 1974.
E.H.Land, Recent advances in retinex theory, Vision Research 26: 7–21, 1986.
M.D'Zmura and P.Lennie, Mechanisms of color constancy, J. Optic. Soc. Amer. 3(10): 1662–1672, October 1986.
S.T.Barnand and W.B.Thompson, Disparity analysis of images, IEEE Trans. Patt. Anal. Mach. Intell. 2(4): 333–340, 1980.
J.Y. Zheng, M. Barth, and S. Tsuji, Qualitative route scene description using autonomous landmark detection, Proc. 3rd Intern. Conf. Comput. Vis. pp. 558–562, Osaka, 1990.
B.K.P.Horn and B.G.Schunk, Determining optical flow, Artificial Intelligence, 17: 185–203, 1981.
D.W. Thompson, Matching from 3-D range models into 2-D intensity scenes, Proc. 1st Intern. Conf. Comput. Vis., pp. 65–72, 1987.
J.Y.Zheng, M.Barth, and S.Tsuji, Autonomous landmark selection for route recognition by a mobile robot. Proc. IEEE Intern. Conf. Robot. Autom. 3: 2004–2009, Sacramento, 1991.
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Zheng, J.Y., Tsuji, S. Panoramic representation for route recognition by a mobile robot. Int J Comput Vision 9, 55–76 (1992). https://doi.org/10.1007/BF00163583
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DOI: https://doi.org/10.1007/BF00163583