An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

Zhang, Kang; Zhong, Rugang; Wu, Aibin; Shi, Zhenwei; Luo, Zhongfei

doi:10.1109/lgrs.2016.2614749

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…Borges et al [176] also segmented points into planes and then calculated the intersection lines between adjacent planes. Ni et al [170] and Kang et al [177] projected 3-D points onto a plane and then identified edge points based on the distribution of the projected points such as the pattern of angular gaps [177]. A region-growing method can connect edge points using point orientations estimated by RANSAC to constrain the growth.…”

Section: ) Segment-based Methods: Fold Edges In 3-d Point Cloudsmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

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Section: ) Segment-based Methods: Fold Edges In 3-d Point Cloudsmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Unlike distance-based rules, statistical rules were used to refine the clusters base on expectation maximization algorithm, where the objective function is log-likelihood measuring how well the probabilistic subset fits the point cloud dataset [23], [24]. Another widely accepted rule is the local feature, which was estimated and clustered based on curvatures [25], [26], vertices and boundaries [27], [28], angle parameters [29], eigenvalues [30], natural quadric shapes [31], dual quadric metric [32], graph [33], and thresholdindependent Bayesian sampling consensus [34]. A recent work in [33] realized a uniform resampling while preserving the local features through normalized Laplacian and a k-nearest-neighbor graph.…”

Section: Fig1 Application Framework Of Automatic Assembly Line Basedmentioning

confidence: 99%

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

Qiu

Zhang

et al. 2020

IEEE Access

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Enormous computation and long data processing time of the online tolerance inspection procedure has been a bottleneck in reducing the production cycle time of automatic assembly line. To reduce time consumption while maintaining accuracy, a fast assembly tolerance inspection method is proposed in this paper using scale-reduced models. First, the general part surface, input by a laser-scanned dense point cloud, is segmented through feature-based clustering. Shape variation estimation are designed to determine reduction ratios between clusters adaptively. Then, assembly simulation method is further advanced to utilize coarse point models with imbalanced density. Finally, numerical experiments are designed to estimate both static and dynamic assembly tolerances as results of tolerance inspection. A case study on a motion component of a rail material transporter shows that the proposed method could reduce 81% of the data processing time compared to traditional practice. The precision loss has minor influence on the inspection results because spatial positioning error is no more than 6% of tolerance interval and 18% of scanning resolution. Moreover, the proposed method has a comprehensive advantage of time and accuracy than two conventional point cloud simplification techniques based on iterative and clustering methods.

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“…If the point set belongs to pole in non-empty voxels, it has a cylindrical feature when the density of point clouds is high. The RANSAC algorithm is used to detect cylinder from point set [41][42][43]. Figure 7 shows the result of cylinder detection, and the black ring is the top view of the detected cylindrical feature.…”

Section: Extraction Of Pole-like Objectmentioning

confidence: 99%

Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

et al. 2018

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Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.

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An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

Cited by 17 publications

References 12 publications

Geometric Primitives in LiDAR Point Clouds: A Review

Geometric Primitives in LiDAR Point Clouds: A Review

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

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