KEYWORDS: Cameras, Imaging systems, Error analysis, Detection and tracking algorithms, 3D modeling, Data modeling, CCD cameras, 3D acquisition, 3D image processing, Computing systems
Finding the position and orientation between a camera and a target with respect to a scene object from n correspondence
points is crucial for many computer and robot vision tasks. With a limited number of correspondence points, the
closed-from solution is applied to solve the pose estimation problem. To estimate the pose between the camera and the
target from the four reference point, a pose estimate model is built with the four projection line between the 3D space
point and 2D image point, under the full perspective projection of the camera. The transformation matrix is determined
by the coordinates of four reference points in camera coordinate system and the target coordinate system respectively.
To figure out the transformation matrix, the distance factor of the four reference points in camera system must be
calculated. Considering the quality of the triangle, the pose estimate model with is simplified, which avoid the iteration,
as while as taking the advantage of the data redundancy. Considering the specific relationship of the four reference
points, the Levenberg-Marquardt algorithm is used to figure out the unknown parameters in the pose estimate model.
Then the position and orientation between the camera and the target is obtained with respect to the coordinate
transformation matrix from the camera coordinates to the target coordinates. In the experiment, both synthetic and real
data are used to examine the accuracy and stability of the pose estimate algorithms with four points. Experiment result
shows the distance measurement precision better than 0.03mm, and the angle measurement precision better than 0.2°.
Using a binocular stereo vision system for 3D coordinate measurement, system calibration is an important factor for
measurement precision. In this paper we present a flexible calibration method for binocular stereo system calibration to
estimate the intrinsic and extrinsic parameters of each camera and the exterior orientation of the turntable's axis which is
installed in front of the binocular stereo vision system to increase the system measurement range. Using a new flexible
planar pattern with four big circles and an array of small circles as reference points for calibration, binocular stereo
calibration is realized with Zhang Plane-based calibration method without specialized knowledge of 3D geometry. By
putting a standard ball in front of the binocular stereo vision system, a sequence pictures is taken at the same by both
camera with a few different rotation angles of the turntable. With the method of space intersection of two straight lines,
the reference points, the ball center at each turntable rotation angles, for axis calibration are figured out. Because of the
rotation of the turntable, the trace of ball is a circle, whose center is on the turntable's axis. All ball centers rotated are in
a plane perpendicular to the axis. The exterior orientation of the turntable axis is calibrated according to the calibration
model. The measurement on a column bearing is performed in the experiment, with the final measurement precision better
than 0.02mm.
KEYWORDS: Data acquisition, 3D modeling, 3D acquisition, Data modeling, Cameras, Image segmentation, Mathematical modeling, Sensors, Optical engineering, 3D scanning
This paper presents a novel scanning-path determination method to choose a next best view, using a combination of the shape-from-silhouette and convex-hull methods to ensure the accuracy and integrity of measured object surface data. A backlight system is used to illuminate the scene to acquire silhouettes from different viewpoints, from which an approximate 3-D surface model can be deduced. The scanning path is determined according to the convex hull of the approximate model so as to specify the movement of a three-axis motion table to achieve automatic measurement. With a mathematical model of a sensor equipped with a color CCD and a line-structured laser, 3-D color data of the object can be obtained integrally and accurately.
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