Television – Special applications
Reexamination Certificate
1998-02-13
2001-10-09
Kelley, Chris (Department: 8613)
Television
Special applications
C382S154000
Reexamination Certificate
active
06300974
ABSTRACT:
DESCRIPTION
The invention relates to a process and a device for locating an object in three-dimensional space and has a large number of applications such as robotics, mapping, aeronautics, etc., in which random movements of a moving solid must be identified for the purposes of position measurements, tracking, industrial process control or deformation measurements.
There are many technical solutions for making these types of measurement or inspection, but not all are easy to use in an industrial environment which may be hostile due to high temperatures and pressures, or radioactivity. Furthermore, not all solutions will be capable of quickly providing results with sufficient accuracy.
Known processes generally consist of equipping the object with targets aimed at by a sensor and the positions of which are known with respect to the sensor; the position of the object, including its orientation, is deduced by considering all the positions of the various targets. There are three categories of typical targets, namely magnetic, ultrasound and optical. The first usually consist of coils with pulsed operation to produce magnetic fields, and systems of this type are used together with magnetic sensors that determine field forces and angles. But they have the disadvantage of a long measurement lag, limited ranges and interferences that may occur between coils when there are ferrous materials inside the fields. Finally, the localization is not very precise.
Ultrasound targets transmit pulse sequences received by several sensors in order to measure the distance between each transmitter and each sensor and to deduce the position of the transmitters by triangulation. These solutions also have disadvantages related to a resolution defect, low precision and risk of interference due to echoes and other noise.
Optical solutions are based on the observation of marks made on the object and visible to cameras or analog image taking means. When several cameras are provided, a stereoscopic view of the environment and the object is made in order to deduce the direction between the object and each of the cameras by separately examining the position of marks on each image, and deducing the position of the object by triangulation. But these systems are expensive due to the large number of cameras, which must also be linked very precisely so that the localization quality is acceptable; and localization is limited to objects present in fairly restricted areas in space, and more precisely at the location at which the observation camera beams intersect, so that cameras must be disassembled and the system must be readjusted for observations at different distances.
Monocular localization is based on the use of a single camera. Some solution methods are analytic and make it necessary to solve an equation or a system of equations to determine the positions of marks starting from their images. They are fast, but sensitive to measurement noise.
Another category consists of iterative solutions in which the position of the marks on the object is estimated then corrected to minimize an error criterion between images recorded by the camera and the images that would be obtained if the marks were in the estimated position. These solutions are precise, not very sensitive to measurement noise and make it possible to use a variable number of marks, but they have the disadvantage that convergence may be fairly slow and the first estimate must be close to the solution, otherwise there is no guarantee of convergence.
One solution is described in the article “Model-based object pose in 25 lines of code” by DeMenthon and Davis, that appeared in the International Journal of Computer Vision, vol. 15, p. 123-141, 1995 and consists of obtaining a first estimate of the position of the marks on the object by an approximate but easy to use algorithm, and then correcting the estimated position by an iterative process in which the position estimates of the marks are projected onto the camera image before applying the approximate algorithm to these projections to give a new estimate of the position of the marks which is more accurate than the previous estimates.
The invention belongs to the family of monocular optical methods and includes the use of an iterative solution process using a first algorithm estimating a position very close to this article; but it has the distinction in particular that it uses a better final estimating algorithm less sensitive to measurement noise, as a result of a particular step improving the determination of the position of marks on the image, and by the nature of the marks that satisfies the requirement for precision in the final determination of the image position.
In its most general formulation, the invention relates to a process for positioning an object carrying marks in space, consisting of determining the positions of traces of marks on an image taken with a camera, and then calculating the positions of the marks with respect to the images taken using the positions of the traces on the image, characterized in that it comprises a step to improve the determination of the positions of traces on the image by modeling the traces using predetermined geometric shape functions and calculating the positions of the shape function that best correspond to the traces. This type of model can estimate the position of the traces with much better precision than the resolution pitch of the camera, so that excellent precision is obtained in the position of the marks, even with low resolution images.
The device used in the process according to the invention preferably comprises marks without any light sources, possibly but not necessarily reflecting, and the image taking means is in the form of a camera.
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Computer Vision and Image Understanding, vol. 63, No. 3, May, pp. 495-511, 1996; article No. 0037, D. Oberkampf, D. DeMenthon, L. Davis, “Iterative Pose Estimation Using Coplanar Feature Points” XP000597554.
Letellier Laurent
Viala Marc
Burns Doane Swecker & Mathis
Commissariat A l'Energie Atomique
Kelley Chris
Vo Tung
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