A plane-based and linear camera calibration technique without considering lens distortion is proposed in a greedy and intuitive framework for the binocular camera system. Characteristic homography matrix and consisten...A plane-based and linear camera calibration technique without considering lens distortion is proposed in a greedy and intuitive framework for the binocular camera system. Characteristic homography matrix and consistency constraints in close range are employed in this calibration. First, in order to calculate the internal geometries of the cameras, total least-square fitting as a robust tool for the geometrical cost function is exploited to recover the accurate principal point of each camera from all the characteristic lines of the homography matrices for all model planes. Secondly, generic prior knowledge of the aspect ratio of pixel cells is incorporated into the system to obtain the exact principal length in each camera. Thirdly, extrinsic geometries are accurately computed for all planar patterns with respect to each monocular camera. Finally, the rigid displacement between binocular cameras can be obtained by imposing the consistency constraints in 3-space geometry. Both simulation and real image experimental results indicate that reasonably reliable results can be obtained by this technique. And the proposed method is sufficient for applications where high precision is not required and can be easily performed by common computer users who are not experts in computer vision.展开更多
文摘A plane-based and linear camera calibration technique without considering lens distortion is proposed in a greedy and intuitive framework for the binocular camera system. Characteristic homography matrix and consistency constraints in close range are employed in this calibration. First, in order to calculate the internal geometries of the cameras, total least-square fitting as a robust tool for the geometrical cost function is exploited to recover the accurate principal point of each camera from all the characteristic lines of the homography matrices for all model planes. Secondly, generic prior knowledge of the aspect ratio of pixel cells is incorporated into the system to obtain the exact principal length in each camera. Thirdly, extrinsic geometries are accurately computed for all planar patterns with respect to each monocular camera. Finally, the rigid displacement between binocular cameras can be obtained by imposing the consistency constraints in 3-space geometry. Both simulation and real image experimental results indicate that reasonably reliable results can be obtained by this technique. And the proposed method is sufficient for applications where high precision is not required and can be easily performed by common computer users who are not experts in computer vision.
