Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction

Three different algorithms for mobile robot vision-based obstacle detection are presented in this paper each based on different assumptions The first two algorithms are qualitative in that they return only yes/no answers regarding the presence of obstacles in the field of view; no 3D reconstruction is performed. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second only assumes that the ground is planar. The third algorithm is quantitative in that it continuously estimates the ground plane and reconstructs partial 3D structures by determining the height above the ground plane of each point in the scene. Experimental results are presented for real and simulated data, and the performance of the three algorithms under different noise levels is compared in simulation. We conclude that in terms of the robustness of performance, the third algorithm is superior to the other two.

[1]  Allen R. Hanson,et al.  Three-dimensional reconstruction under varying constraints on camera geometry for robotic navigation scenarios , 1996 .

[2]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Rakesh Kumar,et al.  Model-dependent inference of 3D information from a sequence of 2D images , 1992 .

[4]  M.J. Daily,et al.  An operational perception system for cross-country navigation , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Bruce A. Draper,et al.  A practical obstacle detection and avoidance system , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[6]  Larry H. Matthies.  Toward stochastic modeling of obstacle detectability in passive stereo range imagery , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Martin Herman,et al.  Obstacle detection for a vehicle using optical flow , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[8]  Hanspeter A. Mallot,et al.  Visual obstacle detection for automatically guided vehicles , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[9]  Z. Zhang.  3D Reconstruction under Varying Constraints on Camera Geometry for Robotic Naviation Scenarios , 1996 .

[10]  Wilfried Enkelmann,et al.  Obstacle detection by evaluation of optical flow fields from image sequences , 1990, Image Vis. Comput..

[11]  Allen R. Hanson,et al.  Qualitative obstacle detection , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Larry H. Matthies,et al.  Perception control for obstacle detection by a cross-country rover , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[13]  Rama Chellappa,et al.  Automatic feature point extraction and tracking in image sequences for unknown camera motion , 1993, 1993 (4th) International Conference on Computer Vision.

[14]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Yiannis Aloimonos,et al.  Obstacle Avoidance Using Flow Field Divergence , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Larry H. Matthies,et al.  Stochastic performance, modeling and evaluation of obstacle detectability with imaging range sensors , 1994, IEEE Trans. Robotics Autom..

[17]  Volker Graefe,et al.  Object detection in real-time , 1991 .

[18]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1995, Int. J. Robotics Res..

[19]  Amnon Shashua,et al.  Projective depth: A geometric invariant for 3D reconstruction from two perspective/orthographic views and for visual recognition , 1993, 1993 (4th) International Conference on Computer Vision.

[20]  Olivier Faugeras,et al.  Motion and Structure from Motion in a piecewise Planar Environment , 1988, Int. J. Pattern Recognit. Artif. Intell..

[21]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[22]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[23]  Thomas S. Huang,et al.  Theory of Reconstruction from Image Motion , 1992 .

[24]  Allen R. Hanson,et al.  Progress in computer vision at the University of Massachusetts , 1989 .