The design, manufacture and test of a custom-made phase mask for use in a high-speed fringe projection system is presented. The mask produces a controlled anisotropic point spread function (PSF) that blurs binary fringes parallel to the fringe direction, to produce high quality greyscale patterns at up to the maximum projection rate, 22,000 frames s-1, of the DMD (Digital Micromirror Device)-based projector. The paper describes the numerical design method based on a binary scatter plate; a polychromatic Fourier optics model to predict the device’s optical performance; a method to design the fringe patterns; the manufacturing process, based on photolithography and reactive ion etching; and experimental validation of the phase mask performance. Noise in the computed height-encoding phase maps is found to be just 22% higher than for 8-bit greyscale fringe patterns produced by traditional temporal integration through a sequence of bit planes, but the projection rate is increased by over two orders of magnitude.
Accurate localisation and characterisation of holes is often required in the field of automated assembly and quality control. Compared to time consuming coordinate measuring machines (CMM), fringe-projection-based 3D scanners offer an attractive alternative as a fast, non-contact measurement technique that provides a dense 3D point cloud of a large sample in a few seconds. However, as we show in this paper, measurement artifacts occur at such hole edges, which can introduce errors in the estimated hole diameter by well over 0.25 mm, even though the estimated hole centre locations are largely unaffected. A compensation technique to suppress these measurement artifacts has been developed, by modelling the artifact using data extrapolated from neighboring pixels. By further incorporating a sub-pixel edge detection technique, we have been able to reduce the root mean square (RMS) diameter errors by up to 9.3 times using the proposed combined method.
KEYWORDS: Manufacturing, Mathematical modeling, Object recognition, Control systems, Factor analysis, 3D metrology, Clouds, 3D modeling, Machine vision, Data modeling, Detection and tracking algorithms, 3D image processing
Object recognition and pose estimation is a fundamental problem in automated quality control and assembly in the manufacturing industry. Real world objects present in a manufacturing engineering setting tend to contain more smooth surfaces and edges than unique key points, making state-of-the-art algorithms that are mainly based on key-point detection, and key-point description with RANSAC and Hough based correspondence aggregators, unsuitable. An alternative approach using maximum likelihood has recently been proposed in which surface patches are regarded as the features of interest1 . In the current study, the results of extending this algorithm to include curved features are presented. The proposed algorithm that combines both surfaces and curves improved the pose estimation by a factor up to 3×, compared to surfaces alone, and reduced the overall misalignment error down to 0.61 mm.
A novel technique for object recognition and localization within a 3D point cloud has been developed, by constructing a likelihood function for the pose vector of a known model in a measured scene. The function is based on surface features in the model and corresponding surface features detected in the scene. Using an optimization algorithm, the maximum of the function was found, corresponding to the 6 degree of freedom (DOF) pose of the model within the scene even in the presence of significant clutter.
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