Low Overlapping Point Cloud Registration Using Line Features Detection

Prokop, Miloš; Shaikh, Salman Ahmed; Kim, Kyoung-Sook

doi:10.3390/rs12010061

Cited by 24 publications

(18 citation statements)

References 25 publications

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“…FN is the number of samples whose real tags belong to positive class but whose predicted value is negative class. The mathematical definition of F1 is shown in Equation (14).…”

Section: Methodsmentioning

confidence: 99%

“…Compared with the two-dimensional image feature extraction method, the threedimensional point cloud feature extraction method is a recent innovation. How to realize an efficient and robust point cloud geometric feature extraction algorithm has been a hot issue in this field in recent years [9][10][11][12][13][14]. Many scholars have extracted the geometric features of point clouds through traditional methods.…”

Section: Introductionmentioning

confidence: 99%

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Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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“…FN is the number of samples whose real tags belong to positive class but whose predicted value is negative class. The mathematical definition of F1 is shown in Equation (14).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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“…However, it also proposes the strict request to the point cloud data. The convergence accuracy of the ICP algorithm mainly depends on the ratio of overlapping regions [14,15]. Previous works stated that corresponding points were difficult to be extracted correctly if the overlapping ratio was under 50% [16,17].…”

Section: Fine Registrationmentioning

confidence: 99%

Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration

Liu

et al. 2021

Remote Sensing

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With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.

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“…The marker-less approaches are commonly used in literature for urban and forest point clouds simultaneously. On the one hand, the feature diversity in urban scenes allows various point cloud strip alignment and multiscan co-registration based on points [26][27][28][29], lines [17,[30][31][32] and planar surfaces [33][34][35]. On the other hand, several point cloud co-registration approaches have been performed in forested areas using the forest structure, tree parameters and virtual points from tree shape analysis.…”

Section: Related Workmentioning

confidence: 99%

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

Fekry

Yao

Cao

et al. 2021

IJGI

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A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.

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Low Overlapping Point Cloud Registration Using Line Features Detection

Cited by 24 publications

References 25 publications

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

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