Reconstruction of 3-D road geometry from images for autonomous land vehicles

A novel algorithm for reconstructing 3-D road geometry from images is presented for the purpose of autonomously navigating land vehicles. The reconstruction is based on an idealized road model: a road is assumed to be generated by a horizontal line segment of a fixed length sweeping in the scene. The constraints that ideal road images must satisfy are expressed as a set of differential equations; the 3-D road geometry is reconstructed by numerically integrating these equations. In order to prevent numerical instability, a correction scheme is proposed for stabilizing computation: at each numerical integration step, the computed solution is modified in such a way that the required constraint is always satisfied. Some examples based on real road images are shown. The inherent ill-posedness of the problem and related technical issues are discussed in detail. >

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

[2]  Daniel DeMenthon,et al.  Range-video fusion and comparison of inverse perspective algorithms in static images , 1990, IEEE Trans. Syst. Man Cybern..

[3]  Jacqueline Le Moigne Domain-dependent reasoning for visual navigation of roadways , 1988, IEEE J. Robotics Autom..

[4]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[5]  Larry S. Davis,et al.  Road Boundary Detection For Autonomous Vehicle Navigation , 1986 .

[6]  Azriel Rosenfeld,et al.  Synthesis of a road image as seen from a vehicle , 1986, Pattern Recognit..

[7]  James L. Crowley,et al.  Navigation for an intelligent mobile robot , 1985, IEEE J. Robotics Autom..

[8]  Rafael M. Inigo,et al.  Sensing Error for a Mobile Robot Using Line Navigation , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  K.-I. Kanatani,et al.  3D Euclidean Versus 2D Non-Euclidean: Two Approaches to 3D Recovery from Images , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Rafael M. Inigo,et al.  Range Measurements by a Mobile Robot Using a Navigation Line , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[12]  T. Poggio,et al.  III-Posed problems early vision: from computational theory to analogue networks , 1985, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[13]  Keith Michael Andress,et al.  Evidence accumulation & flow of control , 1988 .

[14]  Rafael M. Inigo,et al.  Machine Vision Applied to Vehicle Guidance , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Daniel DeMenthon,et al.  A zero-bank algorithm for inverse perspective of a road from a single image , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[16]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[17]  Alberto Elfes,et al.  Sonar-based real-world mapping and navigation , 1987, IEEE J. Robotics Autom..

[18]  Shinji Ozawa,et al.  Analysis of a Road Image as Seen from a Vehicle , 1989, Systems and Computers in Japan.

[19]  Darwin T. Kuan,et al.  Autonomous Robotic Vehicle Road Following , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Jacqueline LeMoigne DOMAIN-DEPENDENT REASONING FOR VISUAL NAVIGATION OF ROADWAYS , 1986 .

[21]  Larry S. Davis,et al.  Road boundary detection in range imagery for an autonomous robot , 1988, IEEE J. Robotics Autom..

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

[23]  Shinji Ozawa,et al.  Visual Navigation of an Autonomous Vehicle Using White Line Recognition , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ramesh Jain,et al.  Road Following Using Vanishing Point , 1987 .