Structured light is a robust and accurate method for 3D range imaging in which one or more light patterns are projected onto the scene and observed with an off-axis camera. Commercial sensors typically utilize DMD- or LCD-based LED projectors, which produce good results but have a number of drawbacks, e.g. limited speed, limited depth of focus, large sensitivity to ambient light and somewhat low light efficiency.
We present a 3D imaging system based on a laser light source and a novel tip-tilt-piston micro-mirror. Optical interference is utilized to create sinusoidal fringe patterns. The setup allows fast and easy control of both the frequency and the phase of the fringe patterns by altering the axes of the micro-mirror. For 3D reconstruction we have adapted a Dual Frequency Phase Shifting method which gives robust range measurements with sub-millimeter accuracy.
The use of interference for generating sine patterns provides high light efficiency and good focusing properties. The use of a laser and a bandpass filter allows easy removal of ambient light. The fast response of the micro-mirror in combination with a high-speed camera and real-time processing on the GPU allows highly accurate 3D range image acquisition at video rates.
Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
Submarine oil and gas pipeline inspection is a highly time and cost consuming task. Using an autonomous
underwater vehicle (AUV) for such applications represents a great saving potential. However, the AUV navigation
system requires reliable localization and stable tracking of the pipeline position. We present a method for robust
pipeline localization relative to the AUV in 3D based on stereo vision and echo sounder depth data. When the
pipe is present in both camera images, a standard stereo vision approach is used for localization. Enhanced
localization continuity is ensured using a second approach when the pipe is segmented out in only one of the
images. This method is based on a combination of one camera with depth information from the echo sounder
mounted on the AUV. In the algorithm, the plane spanned by the pipe in the camera image is intersected with
the plane spanned by the sea floor, to give the pipe position in 3D relative to the AUV. Closed water recordings
show that the proposed method localizes the pipe with an accuracy comparable to that of the stereo vision
method. Furthermore, the introduction of a second pipe localization method increases the true positive pipe
localization rate by a factor of four.
KEYWORDS: 3D image processing, 3D modeling, Image segmentation, 3D metrology, Data modeling, Cameras, Detection and tracking algorithms, Clouds, Projection systems, Structured light
Automatic picking of parts is an important challenge to solve within factory automation, because it can remove tedious
manual work and save labor costs. One such application involves parts that arrive with random position and orientation
on a conveyor belt. The parts should be picked off the conveyor belt and placed systematically into bins. We describe a
system that consists of a structured light instrument for capturing 3D data and robust methods for aligning an input 3D
template with a 3D image of the scene. The method uses general and robust pre-processing steps based on geometric
primitives that allow the well-known Iterative Closest Point algorithm to converge quickly and robustly to the correct
solution. The method has been demonstrated for localization of car parts with random position and orientation. We
believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D
objects in a scene is needed.
A flexible and highly configurable 3D vision system targeted for in-line product inspection is presented. The system includes a low cost 3D camera based on structured light and a set of flexible software tools that automate the measurement process. The specification of the measurement tasks is done in a first manual step. The user selects regions of the point cloud to analyze and specifies primitives to be characterized within these regions. After all measurement tasks have been specified, measurements can be carried out on successive parts automatically and without supervision. As a test case, a measurement cell for inspection of a V-shaped car component has been developed. The car component consists of two steel tubes attached to a central hub. Each of the tubes has an additional bushing clamped to its end. A measurement is performed in a few seconds and results in an ordered point cloud with 1.2 million points. The software is configured to fit cylinders to each of the steel tubes as well as to the inside of the bushings of the car part. The size, position and orientation of the fitted cylinders allow us to measure and verify a series of dimensions specified on the CAD drawing of the component with sub-millimetre accuracy.
An inspection system is developed to replace manual inspection in a production line for car parts. The system, based on projected structured light, combining Gray code and phase shifting and using B/W CCD cameras and multi-media data projectors, provides robust height measurement images with a high resolution. By carefully observing a number of parameters, it is possible to attain this high resolution in a large measurement volume even with low-cost, off-the-shelf components. We have been able to achieve a noise floor in the phase determination of 30 mrad, which is better than the much reported 1 part in 10,000. The use of 4 cameras, 3 projectors and a turning operation allows total coverage of the complex shape part.
A model of normal parts is designed using height measurement images of normal parts. This model represents both expected part dimensions in all camera views as well as normal variations. In order to compare measurements of new parts with the model, an alignment of the images is performed. The deviations between the measured part and the model are analyzed. Deviations outside the normal variation are classified as faults. The system is thus able to find geometrical faults as small as 2x2x0.25 mm in a part that measures roughly 400x400 mm and can decide whether or not to remove a part from the production line. Integrating optical metrology, image processing and robotics, we are able to design a complete system for in-line inspection of car parts with total coverage that is able to keep up with the production cycle time.
HoloVision is a software package for performing digital holography on the Microsoft Windows platform. Basic theory for reconstruction of digitally sampled holograms is presented along with some more specific software implementation details. This includes the Fresnel method, the Convolution method and the Fourier method. A method involving a tilt of the reference wave and magnification through a numerical lens is presented to enlarge the visible region of the reconstructed image. Two different approaches for suppressing the undesired zero-order components are investigated. Examples are included for ordinary intensity images as well as for phase difference images from digital holographic interferometry.
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