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

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

Accurate characterisation of hole geometries by fringe projection profilometry

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 artefacts 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 artefacts has been developed, by modelling the artefact 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