scholarly journals A Novel Surface Descriptor for Automated 3-D Object Recognition and Localization

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 764
Author(s):  
Liang-Chia Chen ◽  
Thanh-Hung Nguyen
Keyword(s):  
Spatial Resolution ◽  
Point Cloud ◽  
Object Segmentation ◽  
Point Clouds ◽  
Mean Deviation ◽  
Icp Algorithm ◽  
Automated Recognition ◽  
Novel Approach ◽  
Regional Area ◽  
Segmented Object

This paper presents a novel approach to the automated recognition and localization of 3-D objects. The proposed approach uses 3-D object segmentation to segment randomly stacked objects in an unstructured point cloud. Each segmented object is then represented by a regional area-based descriptor, which measures the distribution of surface area in the oriented bounding box (OBB) of the segmented object. By comparing the estimated descriptor with the template descriptors stored in the database, the object can be recognized. With this approach, the detected object can be matched with the model using the iterative closest point (ICP) algorithm to detect its 3-D location and orientation. Experiments were performed to verify the feasibility and effectiveness of the approach. With the measured point clouds having a spatial resolution of 1.05 mm, the proposed method can achieve both a mean deviation and standard deviation below half of the spatial resolution.

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.

Point Clouds Registration by Using Depth Images

2014 ◽  
Vol 513-517 ◽  
pp. 4193-4196
Author(s):  
Wen Bao Qiao ◽  
Ming Guo ◽  
Jun Jie Liu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Scale Space ◽  
Target Object ◽  
Icp Algorithm ◽  
Depth Images ◽  
Curvature Scale Space ◽  
Initial Location ◽  
Characteristic Points

In this paper, we propose an efficient way to produce an initial transposed matrix for two point clouds, which can effectively avoid the drawback of local optimism caused by using standard Iterative Closest Points (ICP)[ algorithm when matching two point clouds. In our approach, the correspondences used to calculate the transposed matrix are confirmed before the point cloud forms. We use the depth images which have been carefully target-segmented to find the boundaries of the shapes that reflect different views of the same target object. Then each contour is affected by curvature scale space (CSS)[ method to find a sequence of characteristic points. After that, our method is applied on these characteristic points to find the most matching pairs of points. Finally, we convert the matched characteristic points to 3D points, and the correspondences are there being confirmed. We can use them to compute an initial transposed matrix to tell the computer which part of the first point cloud should be matched to the second. In this way, we put the two point clouds in a correct initial location, so that the local optimism of ICP and its variations can be excluded.

scholarly journals Morphing and Sampling Network for Dense Point Cloud Completion

2020 ◽  
Vol 34 (07) ◽  
pp. 11596-11603 ◽  
Author(s):  
Minghua Liu ◽  
Lu Sheng ◽  
Sheng Yang ◽  
Jing Shao ◽  
Shi-Min Hu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Coarse Grained ◽  
Second Stage ◽  
The Earth ◽  
Novel Approach ◽  
Dense Point ◽  
Chamfer Distance ◽  
Structural Loss ◽  
Two Stages

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

scholarly journals Analytical Determination of Geometric Parameters of the Rotary Kiln by Novel Approach of TLS Point Cloud Segmentation

2020 ◽  
Vol 10 (21) ◽  
pp. 7652
Author(s):  
Ľudovít Kovanič ◽  
Peter Blistan ◽  
Rudolf Urban ◽  
Martin Štroner ◽  
Katarína Pukanská ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Longitudinal Axis ◽  
Geometric Parameters ◽  
Analytical Determination ◽  
Deformation Analysis ◽  
Novel Approach ◽  
Base Method ◽  
Spatial Parameters

This research focused on determining a rotary kiln’s geometric parameters in a non-traditional geodetic way—by deriving them from a survey realized by a terrestrial laser scanner (TLS). The point cloud obtained by TLS measurement was processed to derive the longitudinal axis of the RK. Subsequently, the carrier tires’ geometric parameters and shell of the RK during the shutdown were derived. Manual point cloud selection (segmentation) is the base method for removing unnecessary points. This method is slow but precise and controllable. The proposed analytical solution is based on calculating the distance from each point to the RK’s nominal axis (local radius). Iteration using a histogram function was repeatedly applied to detect points with the same or similar radiuses. The most numerous intervals of points were selected and stored in separate files. In the comparison, we present the conformity of analytically and manually obtained files and derived geometric values of the RK-radiuses’ spatial parameters and coordinates of the carrier tires’ centers. The horizontal (X and Y directions) and vertical (Z-direction) of root–mean–square deviation (RMSD) values are up to 2 mm. RMSD of the fitting of cylinders is also up to 2 mm. The center of the carrier tires defines the longitudinal axis of the RK. Analytical segmentation of the points was repeated on the remaining point cloud for the selection of the points on the outer shell of the RK. Deformation analysis of the shell of the RK was performed using a cylinder with a nominal radius. Manually and analytically processed point clouds were investigated and mutually compared. The calculated RMSD value is up to 2 mm. Parallel cuts situated perpendicularly to the axis of the RK were created. Analysis of ovality (flattening) of the shell was performed. Additionally, we also present the effect of gradually decreasing density (number) of points on the carrier tires for their center derivation.

scholarly journals Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4448
Author(s):  
Jianjian Yang ◽  
Chao Wang ◽  
Wenjie Luo ◽  
Yuchen Zhang ◽  
Boshen Chang ◽  
...  
Keyword(s):  
Coordinate Transformation ◽  
Point Cloud ◽  
Point Clouds ◽  
Icp Algorithm ◽  
Point Pair ◽  
Filtering Method ◽  
Environmental Features ◽  
Modeling Accuracy ◽  
Nonlinear Objective Function ◽  
Voxel Grid

In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.

scholarly journals Semi-automatic extraction of sectional view from point clouds – The case of Ottmarsheim's abbey-church

2014 ◽  
Vol XL-5 ◽  
pp. 343-348 ◽  
Author(s):  
T. Landes ◽  
S. Bidino ◽  
R. Guild
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Cutting Plane ◽  
Mean Deviation ◽  
Surfaces Of Revolution ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Heritage Buildings ◽  
Time Required

Today, elevations or sectional views of buildings are often produced from terrestrial laser scanning. However, due to the amount of data to process and because usually 2D maps are required by customers, the 3D point cloud is often degraded into 2D slices. In a sectional view, not only the portions of the objet which are intersected by the cutting plane but also edges and contours of other parts of the object which are visible behind the cutting plane are represented. To avoid the tedious manual drawing, the aim of this work is to propose a semi-automatic approach for creating sectional views by point cloud processing. The extraction of sectional views requires in a first step the segmentation of the point cloud into planar and non-planar entities. Since in cultural heritage buildings, arches, vaults, columns can be found, the position and the direction of the sectional view must be taken into account before contours extraction. Indeed, the edges of surfaces of revolution depend on the chosen view. The developed extraction approach is detailed based on point clouds acquired inside and outside churches. The resulting sectional view has been evaluated in a qualitative and quantitative way by comparing it with a reference sectional view made by hand. A mean deviation of 3 cm between both sections proves that the proposed approach is promising. Regarding the processing time, despite a few manual corrections, it has saved 40% of the time required for manual drawing.

scholarly journals Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

2014 ◽  
Vol XL-8 ◽  
pp. 891-897 ◽  
Author(s):  
Y. D. Rajendra ◽  
S. C. Mehrotra ◽  
K. V. Kale ◽  
R. R. Manza ◽  
R. K. Dhumal ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Large Object ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Process ◽  
Heritage Site ◽  
Laser Scanners ◽  
Dense Point

Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

Automatic Point Clouds Registration Method Based on Mesh Segmentation

2013 ◽  
Vol 423-426 ◽  
pp. 2587-2590
Author(s):  
Li Hua Fan ◽  
Bo Liu ◽  
Bao Ling Xie ◽  
Qi Chen
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
High Speed ◽  
Point Clouds ◽  
Mesh Segmentation ◽  
Registration Method ◽  
Icp Algorithm ◽  
Computer Aided ◽  
Aided Design ◽  
Region Feature

This paper proposes an automatic point clouds registration method based on High-Speed Mesh Segmentation. The proposed method works fast for doing an initial registration and extracting point clouds region feature. First, the features of the point region are used for matching point cloud regions. Second, matched regions sets are classified for calculating transform matrix of initial registration. Based on the initial registration result the Iterative Closest Point (ICP) algorithm which had been used for accuracy registration to composite point cloud pairs will be applied. The proposed registration approach is able to do automatic registration without any assumptions about their initial positions, and avoid the problems of traditional ICP in bad initial estimate. The proposed method plus with ICP algorithm provides an efficient 3D model for computer-aided engineering and computer-aided design.

scholarly journals An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration

2021 ◽  
Vol 10 (4) ◽  
pp. 204
Author(s):  
Ramazan Alper Kuçak ◽  
Serdar Erol ◽  
Bihter Erol
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Data Systems ◽  
Icp Algorithm ◽  
Matching Algorithm ◽  
Suggested Algorithm ◽  
Registration Process ◽  
Building Facade ◽  
Coarse Registration ◽  
Fine Registration

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

scholarly journals AUTOMATIC RAIL EXTRACTION AND CELARANCE CHECK WITH A POINT CLOUD CAPTURED BY MLS IN A RAILWAY

2018 ◽  
Vol XLII-2 ◽  
pp. 767-771 ◽  
Author(s):  
Y. Niina ◽  
R. Honma ◽  
Y. Honma ◽  
K. Kondo ◽  
K. Tsuji ◽  
...  
Keyword(s):  
Contact Line ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Road Maintenance ◽  
Icp Algorithm ◽  
Railway Company ◽  
Visual Confirmation ◽  
Clearance Model ◽  
The Ideal

Recently, MLS (Mobile Laser Scanning) has been successfully used in a road maintenance. In this paper, we present the application of MLS for the inspection of clearance along railway tracks of West Japan Railway Company. Point clouds around the track are captured by MLS mounted on a bogie and rail position can be determined by matching the shape of the ideal rail head with respect to the point cloud by ICP algorithm. A clearance check is executed automatically with virtual clearance model laid along the extracted rail. As a result of evaluation, the accuracy of extracting rail positions is less than 3 mm. With respect to the automatic clearance check, the objects inside the clearance and the ones related to a contact line is successfully detected by visual confirmation.

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