Single-shot areal profilometry using hyperspectral interferometry with a microlens array

We describe a single-shot technique to measure areal profiles on optically smooth and stepped surfaces for applications where rapid data acquisition in non-cooperative environments is essential. It is based on hyperspectral interferometry (HSI), a technique in which the output of a white-light interferometer provides the input to a hyperspectral imaging system. Previous HSI implementations suffered from inefficient utilisation of the available pixels which limited the number of measured coordinates and/or unambiguous depth range. In the current paper a >20-fold increase in pixel utilisation is achieved through the use of a 2-D microlens array, that leads to a 35×35 channel system with an unambiguous depth range of 0.88 mm

Depth-resolved phase imaging

Traditional full-field interferometric techniques (speckle, moiré, holography etc) encode the surface deformation state of the object under test in the form of 2-D phase images. Over the past 10 years, a family of related techniques (Wavelength Scanning Interferometry, Phase Contrast Spectral Optical Coherence Tomography (OCT), Tilt Scanning Interferometry and Hyperspectral Interferometry) has emerged that allows one to measure the volume deformation state within weakly-scattering objects. The techniques can be thought of as combining the phase-sensing capabilities of Phase Shifting Interferometry and the depth-sensing capabilities of OCT. This paper provides an overview of the techniques, and describes a theoretical framework based on the Ewald sphere construction that allows key parameters such as depth resolution and displacement sensitivity to be calculated straightforwardly for any given optical geometry and wavelength scan range. Finally, the related issue of robust phase unwrapping of noisy 3-D wrapped phase volumes is also described

Object recognition and localisation from 3D point clouds by maximum likelihood estimation

We present an algorithm based on maximum likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’ based algorithms which normally discard such data. Compared to the 6D Hough transform it has negligible memory requirements, and is computationally efficient compared to iterative closest point (ICP) algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degree of freedom (DOF) example is given, followed by a full 6 DOF analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an rms alignment error as low as 0:3 mm

Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features

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

High-throughput single-shot hyperspectral interferometer for areal profilometry based on microlens array integral field unit

A single-shot technique to measure areal profiles on optically smooth and rough surfaces and for applications in non-cooperative environments is presented. It is based on hyperspectral interferometry (HSI), a technique in which the output of a white-light interferometer provides the input to a hyperspectral imaging system. Previous HSI implementations suffered from inefficient utilisation of the available pixels which limited the number of measured coordinates and/or unambiguous depth range. In this paper a >20-fold increase in pixel utilization is achieved through the use of a 2-D microlens array as proposed for integral field units in astronomy applications. This leads to a 35×35 channel system with an unambiguous depth range of 0.88 mm

Single-shot areal profilometry: Towards real-time surface quality control in additive manufacturing

© 2018 The authors and IOS Press. All rights reserved. Hyperspectral Interferometry (HSI) is a recently-proposed technique for measuring 3-D point clouds from an opaque object in a single shot. We propose a new application of HSI enabling single-shot 3D surface measurements of optically rough surfaces commonly found on additively manufactured and machined components. Using an additively manufactured sample, single-shot surface profiles were taken at a fixed distance to capture and reconstruct the surface profile. This enables the single-shot measurements of rough surfaces over many independent channels in a short time

Editorial: photomechanics special issue

The term photomechanics refers to the use of light to determine distributions of quantities such as stress, displacement, strain and temperature in experimental solid and fluid mechanics. This special issue on photomechanics, comprising seven papers, provides a representative cross section of some of the current activity in this field, to illustrate trends in the technique development as well as new areas of application

Single-shot profilometry of rough surfaces using hyperspectral interferometry

The combination of white light interferometry with hyperspectral imaging (“hyperspectral interferometry”) is a recently proposed technique for single-shot measurement of 3D surface profiles. We consider for the first time its application to speckled wavefronts from optically rough surfaces. The intensity versus wavenumber signal at each pixel provides unambiguous range information despite the speckle-induced random phase shifts. Experimental results with samples undergoing controlled rigid body translation demonstrate a measurement repeatability of 460 nm for a bandwidth of approximately 30 nm. Potential applications include roughness measurement and coordinate measurement machine probes where rapid data acquisition in noncooperative environments is essential

Depth-resolved imaging and displacement measurement techniques viewed as linear filtering operations

The last 5 years have seen the emergence of a family of optical interferometric techniques that provide deformation measurements throughout three-dimensional (3-D) weakly scattering materials. They include wavelength scanning interferometry (WSI), tilt scanning interferometry (TSI), phase contrast spectral optical coherence tomography (PC SOCT) and hyperspectral interferometry (HSI) and can be thought of as a marriage between the phase sensing capabilities of Phase Shifting Interferometry and the depth-sensing capabilities of Optical Coherence Tomography. It was recently shown that some closely related 3-D optical imaging techniques can be treated as shift-invariant linear filtering operations. In this paper, we extend that work to include WSI, TSI, PC SOCT and HSI as spatial filtering operations and also relate the properties of their transfer functions in the spatial frequency domain to their spatial resolution and phase sensitivity, for depth-resolved displacement measurements

Accurate characterisation of hole size and location by projected fringe profilometry

The ability to accurately estimate the location and geometry of holes is often required in the field of quality control and automated assembly. Projected fringe profilometry is a potentially attractive technique on account of being non-contacting, of lower cost, and orders of magnitude faster than the traditional coordinate measuring machine (CMM). However, we demonstrate in this paper that fringe projection is susceptible to significant (hundreds of µm) measurement artefacts in the neighbourhood of hole edges, which give rise to errors of a similar magnitude in the estimated hole geometry. A mechanism for the phenomenon is identified based on the finite size of the imaging system’s point spread function and the resulting bias produced near to sample discontinuities in geometry and reflectivity. A mathematical model is proposed, from which a post-processing compensation algorithm is developed to suppress such errors around the holes. The algorithm includes a robust and accurate sub-pixel edge detection method based on a Fourier descriptor of the hole contour. The proposed algorithm was found to reduce significantly the measurement artefacts near the hole edges. As a result, the errors in estimated hole radius were reduced by up to one order of magnitude, to a few tens of µm for hole radii in the range 2-15 mm, compared to those from the uncompensated measurements