State-of-the-art in point cloud analysis

Catalucci, Sofia; Senin, Nicola

doi:10.1088/978-0-7503-2524-0ch2

Cited by 7 publications

(10 citation statements)

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“…However, their implementation into the pipeline is still in its infancy. Good practice guides and machine learning algorithms have been developed to optimise measuring procedure and overcome the constraints relating to the user-dependence of many measurement and characterisation protocols [76,119,120]. Further developments are expected to address the current limitations given by measurements held into harsh environments.…”

Section: Discussionmentioning

confidence: 99%

“…8. After a measurement is carried out, a large amount of data is generated and collected implying excess/redundant surface sampling information, which severely augments the data processing computational time and jeopardises the correct assessment of whether a part conforms to dimensional and geometric specification requirements [120]. Thus, algorithms for the optimisation of data acquisition and simplification that can preserve unaltered the properties and the main features of a measurement are required.…”

Section: User-dependent Constraintsmentioning

confidence: 99%

“…Approaches devoted to the understanding and modelling of the uncertainty associated with the individual points of the point cloud are still in their infancy and the current state of the art in this respect is addressed elsewhere [120]. The two most diffuse approaches are expressed in probabilistic terms and illustrated in Fig.…”

Section: Uncertainty Associated With Point Cloudsmentioning

confidence: 99%

“…The two most diffuse approaches are expressed in probabilistic terms and illustrated in Fig. 9, as reported in [120]. The first configuration (in Fig.…”

Section: Uncertainty Associated With Point Cloudsmentioning

confidence: 99%

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Optical metrology for digital manufacturing: a review

Catalucci

Thompson

Piano

et al. 2022

Int J Adv Manuf Technol

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With the increasing adoption of Industry 4.0, optical metrology has experienced a significant boom in its implementation, as an ever-increasing number of manufacturing processes are overhauled for in-process measurement and control. As such, optical metrology for digital manufacturing is currently a hot topic in manufacturing research. Whilst contact coordinate measurement solutions have been adopted for many years, the current trend is to increasingly exploit the advantages given by optical measurement technologies. Smart automated non-contact inspection devices allow for faster cycle times, reducing the inspection time and having a continuous monitoring of process quality. In this paper, a review for the state of the art in optical metrology is presented, highlighting the advantages and impacts of the integration of optical coordinate and surface texture measurement technologies in digital manufacturing processes. Also, the range of current software and hardware technologies for digital manufacturing metrology is discussed, as well as strategies for zero-defect manufacturing for greater sustainability, including examples and in-depth discussions of additive manufacturing applications. Finally, key current challenges are identified relating to measurement speed and data-processing bottlenecks; geometric complexity, part size and surface texture; user-dependent constraints, harsh environments and uncertainty evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: User-dependent Constraintsmentioning

confidence: 99%

Section: Uncertainty Associated With Point Cloudsmentioning

confidence: 99%

“…The two most diffuse approaches are expressed in probabilistic terms and illustrated in Fig. 9, as reported in [120]. The first configuration (in Fig.…”

Section: Uncertainty Associated With Point Cloudsmentioning

confidence: 99%

See 2 more Smart Citations

Optical metrology for digital manufacturing: a review

Catalucci

Thompson

Piano

et al. 2022

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…The pipeline is similar for the cuboid and for the cylinder. First, the point cloud is partitioned into regions using the direction of the local normal computed by principal component analysis (PCA) in combination with k-means clustering on the local normal (more information on the use of k-means clustering combined with local normal can be found in previous work [51]). For both the cuboid and the cylinder, k-means was performed with k = 6, in order to isolate "faces" oriented according to the main six directions (examples are shown in Figure 10).…”

Section: The Point Cloud Processing Pipelinementioning

confidence: 99%