Volume measurement using 3D Range Imaging

The use of 3D Range Imaging has widespread applications. One of its applications provides us the information about the volumes of different objects. In this paper, 3D range imaging has been utilised to find out the volumes of different objects using two algorithms that are based on a straightforward means to calculate volume. The algorithms implemented succesfully calculate volume on objects provided that the objects have uniform colour. Objects that have multi-coloured and glossy surfaces provided particular difficulties in determining volume

Towards monitored tomographic reconstruction: algorithm-dependence and convergence

The monitored tomographic reconstruction (MTR) with optimized photon flux technique is a pioneering method for X-ray computed tomography (XCT) that reduces the time for data acquisition and the radiation dose. The capturing of the projections in the MTR technique is guided by a scanning protocol built on similar experiments to reach the predetermined quality of the reconstruction. This method allows achieving a similar average reconstruction quality as in ordinary tomography while using lower mean numbers of projections. In this paper, we, for the first time, systematically study the MTR technique under several conditions: reconstruction algorithm (FBP, SIRT, SIRT-TV, and others), type of tomography setup (micro-XCT and nano-XCT), and objects with different morphology. It was shown that a mean dose reduction for reconstruction with a given quality only slightlyvaries with choice of reconstruction algorithm, and reach up to 12.5 % in case of micro-XCT and 8.5 % for nano-XCT. The obtained results allow to conclude that the monitored tomographic reconstruction approach can be universally combined with an algorithm of choice to perform a controlled trade-off between radiation dose and image quality. Validation of the protocol on independent common ground truth demonstrated a good convergence of all reconstruction algorithms within the MTR protocol.This work was partly supported by RFBR (grants) 20-07-00934

Predictive tracking with improved motion models for optical belt sorting

Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type

Real-world comparisons between target-based and targetless point-cloud registration in FARO Scene, Trimble RealWorks and Autodesk Recap

With recent improvements to point cloud processing software, encouragement has been given by software manufacturers and retailers to register individual point clouds without isolated targets. Manufacturer test trials and online tutorials show examples of targetless point cloud registration in action. However, it is believed by the author that these examples, using only a handful of scans in beneficial scanning environments, do not necessarily take into account varied, real-world survey conditions that often take multiple scans to effectively measure. This research project seeks to vigorously test and compare the accuracy of point-cloud registration between target-based and targetless processing methodologies across three common point-cloud software packages—FARO Scene, Trimble Realworks and Autodesk Recap. The project is largely field-work based and experimental in nature using six real-world survey scanning scenarios: 1) a heritage facade survey, 2) a quarry plant / conveyor system survey, 3) an indoor floorplan survey, 4) a tunnel survey, 5) a commercial roof survey, and 6) a wharf monitor survey In each scanning scenario, target-based and targetless point-cloud registration methods have been compared and evaluated as to the accuracy of targetless registration for data extraction. A control survey was undertaken for each scenario using traditional total-station measurements of key scanning targets within the scan area to form a base datum. The scanning data was then processed through each software package with the artificial targets isolated and point clouds registered as normal. The relative points were compared to the control survey and found to align well both horizontally and vertically. The identical (pre-edited) raw scan FLS files were then re-processed through each software package. The artificial targets within the scans (minus the isolated control stations that needed to remain for comparisons) were cut away and a targetless registration was completed for all survey areas utilizing as far as possible the software’s default targetless registration settings. The results were compared to the control survey and it was found that, for most scenarios, the control stations generally aligned horizontally within survey accuracies but vertically showed a lack of accuracy required for the precise nature of terrestrial laser scanning (TLS). By testing across a good cross-section of survey scanning scenarios, it can be demonstrated that targetless point cloud registration has its limitations in accurately portraying certain real-world conditions especially in regard to vertical displacement. Though possibly suited to some surveying scenarios, vigorous quality control and traditional check measurements should be used to support user confidence of the data obtained. Further research into developing better targetless registration algorithms that seek to minimize cloud to cloud vertical distortion would be beneficial to targetless software development/enhancement

Flexible registration method for light-stripe sensors considering sensor misalignments

In many application areas such as object reconstruction or quality assurance, it is required to completely or partly measure the shape of an object or at least the cross section of the required object region. For complex geometries, therefore, multiple views are needed to bypass undercuts respectively occlusions. Hence, a multi-sensor measuring system for complex geometries has to consist of multiple light-stripe sensors that are surrounding the measuring object in order to complete the measurements in a prescribed time. The number of sensors depends on the object geometry and dimensions. In order to create a uniform 3D data set from the data of individual sensors, a registration of each individual data set into a common global coordinate system has to be performed. Stateof- the-art registration methods for light-stripe sensors use only data from object intersection with the respective laser plane of each sensor. At the same time the assumption is met that all laser planes are coplanar and that there are corresponding points in two data sets. However, this assumption does not represent the real case, because it is nearly impossible to align multiple laser planes in the same plane. For this reason, sensor misalignments are neglected by this assumption. In this work a new registration method for light-stripe sensors is presented that considers sensor misalignments as well as intended sensor displacements and tiltings. The developed method combines 3D pose estimation and triangulated data to properly register the real sensor pose in 3D space. © 2017 SPIE

Reverse Engineering of Mechanical Parts: a Template-Based Approach

Abstract Template-Based reverse engineering approaches represent a relatively poorly explored strategy in the field of CAD reconstruction from polygonal models. Inspired by recent works suggesting the possibility/opportunity of exploiting a parametric description (i.e. CAD template) of the object to be reconstructed in order to retrieve a meaningful digital representation, a novel reverse engineering approach for the reconstruction of CAD models starting from 3D mesh data is proposed. The reconstruction process is performed relying on a CAD template, whose feature tree and geometric constraints are defined according to the a priori information on the physical object. The CAD template is fitted upon the mesh data, optimizing its dimensional parameters and positioning/orientation by means of a particle swarm optimization algorithm. As a result, a parametric CAD model that perfectly fulfils the imposed geometric relations is produced and a feature tree, defining an associative modelling history, is available to the reverse engineer. The proposed implementation exploits a cooperation between a CAD software package (Siemens NX) and a numerical software environment (MATLAB). Five reconstruction tests, covering both synthetic and real-scanned mesh data, are presented and discussed in the manuscript; the results are finally compared with models generated by state of the art reverse engineering software and key aspects to be addressed in future work are hinted at. Highlights A novel CAD reconstruction method fitting a CAD template model to mesh data. A feature-based parametric-associative modelling history is retrieved. Fitting process is controlled by a Particle Swarm Optimization algorithm. Accuracy of reconstructed models is comparable/better than state of the art results. Computational costs and required time are at the moment considerable

Near Infrared Thermal Imaging for Process Monitoring in Additive Manufacturing

This work presents the design and development of a near infrared thermal imaging system specifically designed for process monitoring of additive manufacturing. The overall aims of the work were to use in situ thermal imaging to develop methods for monitoring process parameters of additive manufacturing processes. The main motivations are the recent growth in use of additive manufacturing and the underutilisation of near infrared camera technology in thermal imaging. The combination of these two technologies presents opportunities for unique process monitoring methods which are demonstrated here. A thermal imaging system was designed for monitoring the electron beam melting process of an Arcam S12. With this system a new method of dynamic emissivity correction based on tracking the melted material is shown. This allows for the automatic application of emissivity values to previously melted areas of a layer image. This reduces the potential temperature error in the thermal image caused by incorrect emissivity values or the assumption of a single value for a whole image. Methods for determining materials properties such as porosity and tensile strength from the in situ thermal imaging are also shown. This kind of analysis from in situ images is the groundwork for allowing part properties to be tuned at build time and could remove the need for post build testing that would determine if it is suitable for use. The system was also used to image electron beam welding and gas tungsten arc welding. With the electron beam welding of dissimilar metals, the thermal images were able to show the preheating effect that the melt pool had on the materials, the suspected reason for the process’s success. For the gas tungsten arc welding process analysis methods that would predict weld quality were developed, with the aim of later integrating these into the robotic welding process. Methods for detecting the freezing point of the weld bead and tracking slag spots were developed, both of which could be used as indicators of weld quality or defects. A machine learning algorithm was also applied to images of pipe welding on this process. The aim of this was to develop an image segmentation algorithm that could be used to measure parts of the weld in process and inform other analysis, like those above