Cutting Plane Based Cylinder Fitting Method With Incomplete Point Cloud Data for Digital Fringe Projection

Yu, Changzhi; Ji, Fang; Xue, Juan

doi:10.1109/access.2020.3016424

Cited by 13 publications

(7 citation statements)

References 44 publications

(74 reference statements)

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“…. By calculating these seven parameters, one cylinder can be uniquely determined [28]. e cylinder fitting method used in this paper is described in detail as follows:…”

Section: Visual Positioning Algorithmmentioning

confidence: 99%

A Point Cloud-Based Feature Recognition and Path Planning Method

Chen

Wang

Huang

2022

Shock and Vibration

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Based on the research background of in situ automatic ultrasonic phased array inspection of irregular porous castings, due to the limited in situ inspection stations, complex shape of irregular porous castings, and extreme multireflection structural features, it is necessary to identify the positioning inspection features among multiple features to be inspected and plan the optimal inspection path. This research is interested in the porous location recognition and its detection path planning of irregular porous castings. For this, a point cloud-based multifeature contour recognition and location algorithm were proposed to simultaneously extract and locate the hole feature and cylindrical feature from an irregular porous castings. Furthermore, a detection path planning method was put forward to search the shortest robot’s detection path based on the above acquired features by visual recognition and positioning technology. First, through the calibration of the industrial robot tool coordinate system and the internal and external parameters of the camera, the “EyeinHand” hand-eye conversion relationship was established. Second, the robot vision system collects the point cloud information of the area to be inspected and performs point cloud splicing, the accuracy of the original data, the removal of noise such as invalid points, outliers and internal noise points, on this basis, the boundary curve of the hole to be inspected was extracted, the cylindrical equation was fitted, its geometric center was calculated, and the central coordinates and axis direction of the contour of the hole to be inspected were obtained. Finally, all the detection paths were traversed through the multibranch tree to obtain the optimal detection path of the detection points of multiple targets. The experimental results show that the positioning accuracy of the feature of the hole to be inspected by the vision system is 0.107 mm, the aperture extraction accuracy is 0.002 mm, the cylinder fitting accuracy is 0.04 mm, and the calculation accuracy of the angle between the two axes is within 0.4. When the number of features to be inspected is different, the average moving distance can be saved by 10.7% by using the end effector after path optimization. The feasibility of in situ automatic ultrasonic phased array detection for irregular porous castings using by visual positioning is verified.

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“…. By calculating these seven parameters, one cylinder can be uniquely determined [28]. e cylinder fitting method used in this paper is described in detail as follows:…”

Section: Visual Positioning Algorithmmentioning

confidence: 99%

A Point Cloud-Based Feature Recognition and Path Planning Method

Chen

Wang

Huang

2022

Shock and Vibration

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“…First, a bilateral filtering method is used to analyze the point cloud in order to lessen the impact of outliers and noisy points. The precision of the cylindrical point cloud fitting is then increased by choosing the isometric cutting plane of the point cloud, using the edge points on the cutting plane for ellipse fitting and the ellipse center point clustering for situating the cylindrical axis based on RANSAC algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator

Liu

Sui

et al. 2023

Journal of Sensors

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The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real-time localization and map-matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41 × 10 − 5 m 2 for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real-time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.

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“…3D modeling [4,5], tree biomass estimation [6,7] and other fields. To enable good application prospects in various fields, researchers have proposed a series of geographical object recognition (including ground/terrain, roads, power lines [8,9], trees [10,11], water bodies, buildings and so on), classification and extraction algorithms based on LiDAR point clouds.…”

Section: Introductionmentioning

confidence: 99%

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

Lin

Wang

et al. 2022

IEEE Access

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In three-dimensional (3D) airborne light detection and ranging (LiDAR) point-cloud data acquisition, noise point clusters (such as cloud, birds and incomplete scanning ground points) and isolated points are usually generated in the scanning process. Detection and elimination of these noise points directly affect the subsequent processing efficiency of the LiDAR point clouds. In this paper, a noise detection method from airborne LiDAR data based on spatial hierarchical directional relationship and region growing algorithm is proposed. First, the original airborne LiDAR points are divided into regular 3D grids, and the maximum point density unit is searched adaptively to select the initial surface seed points for region growing algorithm. Then, the spherical neighborhood is constructed with the initial seed point as the center, and fourteen main growth directions are generated based on the 3D space topology. Second, candidate seed points in each main direction are determined by the distance threshold. Finally, all LiDAR points are iteratively executed using candidate seed points as new region growing seed points. This paper selects two mountain terrain scenes with different cloud contents as the study area, and the precision, recall rates and F1-score of the proposed method reach 99.8%, 100% and 99.3%, respectively. This method can detect point-cloud clusters and isolated points, thus simplifying the LiDAR point clouds, providing basic support for the subsequent accurate data processing and analysis.

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Cutting Plane Based Cylinder Fitting Method With Incomplete Point Cloud Data for Digital Fringe Projection

Cited by 13 publications

References 44 publications

A Point Cloud-Based Feature Recognition and Path Planning Method

A Point Cloud-Based Feature Recognition and Path Planning Method

A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

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