Constrained self-calibration

This paper focuses on the estimation of the intrinsic camera parameters and the trajectory of the camera from an image sequence. Intrinsic camera calibration and pose estimation are the prerequisites for many applications involving navigation tasks, scene reconstruction, and merging of virtual and real environments. Proposed and evaluated is a technical solution to decrease the sensitivity of self-calibration by placing easily identifiable targets of known shape in the environment. The relative position of the targets need not be known a priori. Assuming an appropriate ratio of size to distance these targets resolve known ambiguities. Constraints on the target placement and the cameras' motions are explored. The algorithm is extensively tested in a variety of real-world scenarios.

[1]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[2]  J. P. Mellor,et al.  Enhanced Reality Visualization in a Surgical Environment , 1995 .

[3]  Kokichi Sugihara,et al.  Location of robot using sparse visual information , 1988 .

[4]  Roger Tsai,et al.  Synopsis of recent progress on camera calibration for 3D machine vision , 1989 .

[5]  Steven K. Feiner,et al.  Knowledge-based augmented reality , 1993, CACM.

[6]  Jake K. Aggarwal,et al.  Structure from stereo-a review , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Allen R. Hanson,et al.  Robust methods for estimating pose and a sensitivity analysis , 1994 .

[8]  Michael Drumheller,et al.  Mobile Robot Localization Using Sonar , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[10]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[11]  Ingemar J. Cox Blanche: Position Estimation for an Autonomous Robot Vehicle , 1990, Autonomous Robot Vehicles.

[12]  John Oliensis,et al.  A Critique of Structure-from-Motion Algorithms , 2000, Comput. Vis. Image Underst..

[13]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[14]  S. Bougnoux,et al.  From projective to Euclidean space under any practical situation, a criticism of self-calibration , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).