Measuring the distance and orientation of a planar surface using nonstructured lighting-3-D measurement system for indoor mobile robots

This paper describes a novel method for measuring the distances to planar surfaces and their orientations. The measurement system consists of a TV camera and two ordinary lamps. The lamps are switched on alternately, and images under each lighting condition are taken. If the two light sources are arranged at appropriate positions with respect to the camera, the peak positions in the two images directly give the surface orientation and the perpendicular distance to the plane. By measuring the two peak positions, this method promises to speed up the acquisition of geometrical information on an entire scene considerably because the distance and orientation can be obtained without analyzing range maps. Since the equipment setup is very simple, the proposed technique will be useful, for example, for real-world robotic applications such as navigation of indoor mobile robots. The experimental results on the error of measurement show that the method is sufficient for such purposes.

[1]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[2]  N. A. Finkelstein,et al.  Light Calculations and Measurements , 1956 .

[3]  Gruia-Catalin Roman,et al.  A parallel algorithm for incremental stereo matching on SIMD machines , 1991, IEEE Trans. Robotics Autom..

[4]  James D. Foley,et al.  Fundamentals of interactive computer graphics , 1982 .

[5]  M. Turk,et al.  A simple, real-time range camera , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  George C. Stockman,et al.  Surface Orientation from a Projected Grid , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Francois Blais,et al.  Range imaging sensors development at NRC Laboratories , 1989, [1989] Proceedings. Workshop on Interpretation of 3D Scenes.

[9]  Junichi Hoshino,et al.  Region-based reconstruction of an indoor scene using an integration of active and passive sensing techniques , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[10]  Yoshiaki Shirai,et al.  A scene description method using three-dimensional information , 1979, Pattern Recognit..

[11]  Jan-Peter Muller,et al.  Real-Time Stereo Matching Using Transputer Arrays for Close-Range Applications , 1988, MVA.

[12]  Ray A. Jarvis,et al.  A Perspective on Range Finding Techniques for Computer Vision , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Kim L. Boyer,et al.  Color-Encoded Structured Light for Rapid Active Ranging , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[15]  Josef Kittler,et al.  Robust estimation of shape parameters , 1990, BMVC.

[16]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..

[17]  Jorge L. C. Sanz,et al.  Advances in Machine Vision , 1988, Springer Series in Perception Engineering.

[18]  Olivier D. Faugeras,et al.  Representing Stereo Data with the Delaunay Triangulation , 1990, Artif. Intell..

[19]  Takashi Yoshimi,et al.  Multi Light Sources Range Finder System , 1991, MVA.

[20]  Ronald Lumia,et al.  A pseudo-interferometric laser range finder for robot applications , 1989, IEEE Trans. Robotics Autom..

[21]  Beno Benhabib,et al.  Constraints on quadratic-curved features under perspective projection , 1992, Image Vis. Comput..