An obstacle shape estimating apparatus and a method thereof, includes: a processor configured to receive a the sensing signal from at least one ultrasonic sensor at a predetermined cycle, to generate positions of one or more obstacles according to distance values of an ultrasonic sensor by estimating the distance values of the ultrasonic sensor based on a sensing signal, and to generate obstacle shape information according to positions of remaining obstacles after deleting a position of an obstacle corresponding to a virtual distance value and a position of an obstacle which does not satisfy a validation condition among the positions of the one or more obstacles; and a storage configured to store data and an algorithm driven by the processor, and the obstacle shape information generated by the processor.

A measurement device includes an analyzer configured to analyze a diffraction image of X-rays scattered from a subject; estimate a surface contour shape of a measurement area of the subject; extract feature data from shape information, and determine shape parameters for representing the surface contour shape; calculate a theoretical scattering intensity of each of the scattered X-rays when values of the shape parameters are changed; calculate a difference between a measured scattering intensity of each scattered X-ray and the corresponding theoretical scattering intensity, and generate a regression model of a relationship between a corresponding value of the shape parameter and the difference for each shape parameter; extract one shape parameter candidate value reducing the difference from the regression model, and calculate a theoretical scattering intensity of the shape parameter candidate value; and estimate the value of the shape parameter minimizing the difference while repeatedly changing the shape parameter candidate value.

A measurement device includes an analyzer configured to analyze a diffraction image of X-rays scattered from a subject; estimate a surface contour shape of a measurement area of the subject; extract feature data from shape information, and determine shape parameters for representing the surface contour shape; calculate a theoretical scattering intensity of each of the scattered X-rays when values of the shape parameters are changed; calculate a difference between a measured scattering intensity of each scattered X-ray and the corresponding theoretical scattering intensity, and generate a regression model of a relationship between a corresponding value of the shape parameter and the difference for each shape parameter; extract one shape parameter candidate value reducing the difference from the regression model, and calculate a theoretical scattering intensity of the shape parameter candidate value; and estimate the value of the shape parameter minimizing the difference while repeatedly changing the shape parameter candidate value.

In measuring a dimension of an object to be measured W made of a single material, a plurality of transmission images of the object to be measured W are obtained by using an X-ray CT apparatus, and then respective projection images are generated. The projection images are registered with CAD data used in designing the object to be measured W. The dimension of the object to be measured W is calculated by using a relationship between the registered CAD data and projection images. In such a manner, high-precision dimension measurement is achieved by using several tens of projection images and design information without performing CT reconstruction.

Method and related apparatus for determining the geometrical dimensions of a wheel, or at least one part of a wheel, with particular reference to vehicle wheels, in the context of a wheel maintenance process. This method uses contactless sensors which comprise a scanning radar system, preferably a millimeter-wave radar system, to scan the wheel, or at least one part of the wheel, quickly and accurately.

Method and related apparatus for determining the geometrical dimensions of a wheel, or at least one part of a wheel, with particular reference to vehicle wheels, in the context of a wheel maintenance process. This method uses contactless sensors which comprise a scanning radar system, preferably a millimeter-wave radar system, to scan the wheel, or at least one part of the wheel, quickly and accurately.

In a shape measuring method a scattering intensity profile for a first electromagnetic wave is acquired for a substrate having a pattern thereon. A first expected scattering intensity profile for a first virtual structure corresponding to a first parameter group of first parameters including an attention parameter is acquired by a first simulation. A first convergence value is calculated for each of the first parameters in a first fitting process based on the scattering intensity profile and the first expected scattering intensity profile. A second expected scattering intensity profile is then acquired for a second virtual structure corresponding to a second parameter group of second parameters, which includes the attention parameter fixed to the first convergence value. A second convergence value for each of the second parameters is then calculated in a second fitting process based on the scattering intensity profile and the second expected scattering intensity profile.

In a shape measuring method a scattering intensity profile for a first electromagnetic wave is acquired for a substrate having a pattern thereon. A first expected scattering intensity profile for a first virtual structure corresponding to a first parameter group of first parameters including an attention parameter is acquired by a first simulation. A first convergence value is calculated for each of the first parameters in a first fitting process based on the scattering intensity profile and the first expected scattering intensity profile. A second expected scattering intensity profile is then acquired for a second virtual structure corresponding to a second parameter group of second parameters, which includes the attention parameter fixed to the first convergence value. A second convergence value for each of the second parameters is then calculated in a second fitting process based on the scattering intensity profile and the second expected scattering intensity profile.

Disclosed herein is a system for profiling holes in non-opaque samples. The system includes: (i) an e-beam source configured to project an e-beam into an inspection hole in a sample, such that a wall of the inspection hole is struck and a localized electron cloud is produced; (ii) a light sensing infrastructure configured to sense cathodoluminescent light, generated by the electron cloud; and (iii) a computational module configured to analyze the measured signal to obtain the probed depth at which the wall was struck. A lateral offset, and/or orientation, of the e-beam is controllable, so as to allow generating localized electron clouds at each of a plurality of depths inside the inspection hole, and thereby obtain information at least about a two-dimensional geometry of the inspection hole.

Disclosed herein is a system for profiling holes in non-opaque samples. The system includes: (i) an e-beam source configured to project an e-beam into an inspection hole in a sample, such that a wall of the inspection hole is struck and a localized electron cloud is produced; (ii) a light sensing infrastructure configured to sense cathodoluminescent light, generated by the electron cloud; and (iii) a computational module configured to analyze the measured signal to obtain the probed depth at which the wall was struck. A lateral offset, and/or orientation, of the e-beam is controllable, so as to allow generating localized electron clouds at each of a plurality of depths inside the inspection hole, and thereby obtain information at least about a two-dimensional geometry of the inspection hole.