Application of the 'Iterative Closest Point' Algorithm for Determination of Rotation Axis of a Turntable

2013 ◽  
Vol 199 ◽  
pp. 273-278
Author(s):  
Ireneusz Wróbel
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Rotation Axis ◽  
Rapid Development ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Mechanical Parts ◽  
Scanning Process ◽  
Iterative Closest Point Algorithm

Reverse engineering [ is a field of technology which has been under rapid development for several recent years. Optic scanners are basic devices used as reverse engineering tools. Point cloud describes the shape of a scanned object. Automatic turntable is a device which enables a scanning process from different viewing angles. In the paper, the algorithm is described which has been used for determination of rotation axis of a turntable. The obtained axis constitutes the base for an aggregation of particular point clouds into single resultant common cloud describing the shape of the scanned object. Usability of this algorithm for precise scanning of mechanical parts was validated, precision of shape replication was also evaluated.

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

2021 ◽  
pp. 1-16
Author(s):  
Ghazanfar Ali Shah ◽  
Jean-Philippe Pernot ◽  
Arnaud Polette ◽  
Franca Giannini ◽  
Marina Monti
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Computer Assisted ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Starting Point ◽  
Cad Models ◽  
Incomplete Datasets

Abstract This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

Image Registration for Multimodal Inspection of Mechanical Parts

2003 ◽  
Author(s):  
Yanyan Wu ◽  
Chunhe Gong
Keyword(s):  
Image Registration ◽  
Point Cloud ◽  
Computational Algorithm ◽  
Iterative Closest Point ◽  
Research Focus ◽  
Registration Accuracy ◽  
Multimodal Registration ◽  
Mechanical Parts ◽  
Iterative Closest Point Algorithm ◽  
Inspection Data

Image registration is the process of aligning the corresponding features of images in the same coordinate system. Multimodal registration has been widely used in medical imaging and geographic imaging. However, it has not been broadly applied in the inspection imaging of mechanical parts. Multimodal registration can improve inspection accuracy and quality by combining complementary inspection data from different inspection methods, or “modalities”. The research focus of this work is to develop a computational algorithm to register a CMM point cloud with a CT image in the 2-D (planar) domain. Dealing with outliers is the major concern for achieving required registration accuracy. Targeting solving this problem, a new registration metric is proposed in this work, which makes application of the traditional ICP (Iterative Closest Point) algorithm robust, by optimizing the search for closest points.

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

scholarly journals A HYBRID MODEL FOR THE REVERSE ENGINEERING OF THE MILAN CATHEDRAL. CHALLENGES AND LESSON LEARNT

2021 ◽  
Author(s):  
Franco Spettu ◽  
Simone Teruggi ◽  
Francesco Canali ◽  
Cristiana Achille ◽  
Francesco Fassi
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Learning Algorithm ◽  
A Priori ◽  
Point Clouds ◽  
Logical Structure ◽  
Cloud Data ◽  
Survey Techniques ◽  
Periodic Maintenance ◽  
Maintenance And Inspection

Cultural Heritage (CH) 3D digitisation is getting increasing attention and importance. Advanced survey techniques provide as output a 3D point cloud, wholly and accurately describing even the most complex architectural geometry with a priori established accuracy. These 3D point models are generally used as the base for the realisation of 2D technical drawings and 3D advanced representations. During the last 12 years, the 3DSurveyGroup (3DSG, Politecnico di Milano) conduced an omni-comprehensive, multi-technique survey, obtaining the full point cloud of Milan Cathedral, from which were produced the 2D technical drawings and the 3D model of the Main Spire used by the Veneranda Fabbrica del Duomo di Milano (VF) to plan its periodic maintenance and inspection activities on the Cathedral. Using the survey product directly to plan VF activities would help to skip a long-lasting, uneconomical and manual process of 2D and 3D technical elaboration extraction. In order to do so, the unstructured point cloud data must be enriched with semantics, providing a hierarchical structure that can communicate with a powerful, flexible information system able to effectively manage both point clouds and 3D geometries as hybrid models. For this purpose, the point cloud was segmented using a machine-learning algorithm with multi-level multi-resolution (MLMR) approach in order to obtain a manageable, reliable and repeatable dataset. This reverse engineering process allowed to identify directly on the point cloud the main architectonic elements that are then re-organised in a logical structure inserted inside the informative system built inside the 3DExperience environment, developed by Dassault Systémes.

An Iterative Closest Point Algorithm Based on Biunique Correspondence of Point Clouds for 3D Reconstruction

2015 ◽  
Vol 35 (5) ◽  
pp. 0515003 ◽  
Author(s):  
韦盛斌 Wei Shengbin ◽  
王少卿 Wang Shaoqing ◽  
周常河 Zhou Changhe ◽  
刘昆 Liu Kun ◽  
范鑫 Fan Xin
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

scholarly journals Iterative K-Closest Point Algorithms for Colored Point Cloud Registration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5331
Author(s):  
Ouk Choi ◽  
Min-Gyu Park ◽  
Youngbae Hwang
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Iterative Closest Point ◽  
Depth Images ◽  
The Cost ◽  
Soft Matching ◽  
Colored Point ◽  
Refinement Algorithm

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

Converting Point Cloud to Revolved Solids

2013 ◽  
Vol 371 ◽  
pp. 468-472
Author(s):  
Mircea Viorel Drăgoi ◽  
Slobodan Navalušić
Keyword(s):  
Reverse Engineering ◽  
Cross Section ◽  
Software Package ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
3D Scanning ◽  
Main Drawback ◽  
Mass Properties

3D scanning is one of the basic methods to gather data for reverse engineering. The main drawback of 3D scanning is that its output - the point cloud - can never be used directly to define surfaces or solids useful to reconstruct the electronic 3D model of the scanned part.The paper presents a piece of software designed in VisualLISP for AutoCAD, software that acts as a point cloud to 3D primitives converter. The novelty consists of the method used to find the parameters of the primitive that best fits to the point cloud: the mass properties of regions are used to find the center of a cones cross section. Parts have been scanned and the point clouds processed. The results obtained prove the correctness of the algorithm and of the method applied. A piece of software that processes the point cloud in order to find the 3D primitive that it fits the best has been developed. The output is the 3D primitive that successfully and accurate replaces the point cloud. Some adjacent tools were designed, so the entire software package becomes a useful tool for the reverse engineering user. The ways the researches can be continued and developed are foreseen, as well

scholarly journals EQUAL PROPORTION REPRODUCTION METHOD OF GROTTO BASED ON POINT CLOUD

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 1215-1219
Author(s):  
P. Wei ◽  
A. Li ◽  
M. Hou ◽  
L. Zhu ◽  
D. Xu ◽  
...  
Keyword(s):  
3D Printing ◽  
Point Cloud ◽  
Laser Scanning ◽  
New Technologies ◽  
Rapid Development ◽  
Point Clouds ◽  
Equal Proportion ◽  
3D Laser Scanning ◽  
Printing Technology ◽  
3D Printing Technology

<p><strong>Abstract.</strong> The rapid development of 3D laser scanning and 3D printing technology provides new technologies and ideas for cultural relic protection and reproduction. Aiming at the requirement of equal proportional reproduction of large-scale grottoes, this paper takes the point cloud data of the 18th Cave of Yungang Grottoes obtained by 3D laser scanning as an example, and proposes a data processing and reproduction block partitioning method for equal proportion reproduction. The Cyclone, Geomagic and AutoCAD software were used to construct the 3D model of the grotto, and the 3D printing technology was used to realize the secondary design and model print. Among them, the research focuses on the modeling of massive point clouds and the method of model partitioning based on voxels. It can meet the requirements of movable and assembly while realizing the equal proportional reproduction of the whole grotto. The research results and application can be a good reference for the future grotto reproduction work.</p>

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