Using 2-Lines Congruent Sets for Coarse Registration of Terrestrial Point Clouds in Urban Scenes

Xu, Ershuai; Xu, Zhihua; Yang, Keming

doi:10.1109/tgrs.2021.3128403

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…In addition to the aforementioned keypoint detection methods, 3D descriptors such as FPFH [29], SHOT [30], ROPS [31], binary shape context [32], and 3D DAISY [33] are used to describe these keypoints and are utilized in the process of matching. On the other hand, some methods extract linear features [34]- [36] or planar features [37]- [39] as features from point clouds and use them in the process of registration. In this type of methods the transformation is computed by the matched features and this may be used in voting schemes like RANSAC.…”

Section: A Related Workmentioning

confidence: 99%

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

Ghorbani,

Chen,

Hollaus

et al. 2024

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

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The utilization of terrestrial laser scanning (TLS) and airborne laser scanning (ALS ) point cloud data in forest inventory studies has significantly increased. Fusing of TLS and ALS point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds in forest environments faces various challenges, including unstable features, extensive occlusions, different viewpoints, and differences in point cloud densities. To address these intricate challenges, this study introduces an automated and robust method for co-registering TLS and ALS point clouds based on the correspondence of individual tree locations in forest environments. Recently, tree location-based methodologies have been advanced to grapple with these complexities in forest environments. However, many of these methods are highly sensitive to the accuracy of tree positioning. The proposed approach aims to reduce sensitivity to individual tree positioning accuracy. Initially, the positions of individual trees in both TLS and ALS data are extracted. Then, a filtering approach is applied to eliminate positions with low potential for corresponding matches in the TLS and ALS dataset. S ince larger trees in the TLS data have a higher potential for corresponding matches in the ALS data, an iterative process is applied to identify correspondences between trees in both datasets. After estimating transformation parameters, the coregistration process is executed. The proposed method is applied on six datasets with varying forest complexities. The results demonstrate a high success rate up to 100% if the starting position of the TLS plots are located within ~4 hectares (~2000 trees). Additionally, the potential of the proposed method for co-registering TLS data with ALS data across different search areas and varying number of trees is evaluated in detail. The outcomes indicate that successful co-registration of TLS plot with 50 m diameter to ALS data is successful in the best case within a search radius of approximately 113 hectares (~60,000 tree locations) and in the worst case for around 20 hectares (~10,000 tree locations) depending on the forest complexity.T his paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. It is IEEE style to display support information, including sponsor and financial support acknowledgment, here and not in an acknowledgment section at the end of the article. For example, "This work was supported in part by the U.S. Department of Commerce under Grant BS123456." The name of the corresponding author appears after the financial information, e.g.

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Section: A Related Workmentioning

confidence: 99%

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

Ghorbani,

Chen,

Hollaus

et al. 2024

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

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“…However, when the amount of points is huge, enormous four-point congruent are constructed which causes poor efficiency. In order to further reduce the matching primitives, Xu et al [18] took the line feature in the point cloud scene as the matching primitives. Similar to the four-point congruent sets, they completed the 4-DOF urban scene registration by constructing 2-lines congruent sets (2LCS).…”

Section: Correspondence Matchingmentioning

confidence: 99%

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Li¹,

Wei²,

Xie³

et al. 2022

Preprint

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Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art.Code is available at https://github.com/WPC-WHU/GROR.

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“…For comparison with some state-of-the-art researches, three well-known initial alignment algorithms are introduced as baseline, namely, the point-based method (Super4PCS) [26], the 2D line-based method (2DLINE) [15] and the 3D line-based method (2LCS) [46]. Table Ⅲ summarizes the rotation error and translation error of Super4PCS, 2DLINE, 2LCS and the proposed method, respectively.…”

Section: Comparison and Analysismentioning

confidence: 99%

“…It is because that: (a) When aligning the experimental datasets, it used the voxel grid filter method to down-sample the input point cloud to a suitable average point-to-point distance, which will dramatically reduce the extracted 3D lines number and increase running speed, while the other three methods just conducted on the original datasets. For example, the pairwise point cloud Par1 and Par2 are the common pairwise point cloud used in the original experiments of 2LCS [46] and in our experiment, and the line feature extracting method used of the both methods is proposed by Lu et al [49], but there exists huge difference in the number of extracted lines (154 and 194 in 2LCS method, 288 and 386 in our method). (b) During calculating the candidate transformation parameters, 2LCS used RANSAC scheme, while what used in 2DLINE and our method is the traversing strategy.…”

Section: Comparison and Analysismentioning

confidence: 99%

“…This method converts the 3D point cloud into 2D image for registration, additional errors will be introduced during the process of conversion and inverse conversion. Xu et al [46] proposed a coarse registration method for urban point clouds based on 2-lines congruent sets (2LCS). First, the vertical and horizontal direction lines were extracted to construct the 2-lines bases; Then, according to the geometric character, the congruent 2-lines bases were identified and the 2LCS was formed; Finally, transformation parameters were estimated using the 2LCS.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

Xiong

et al. 2022

IEEE Access

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The fully automatic registration of 3D terrestrial laser scanning (TLS) point clouds, which is the first step in the usage of point clouds, is a highly challenging task in Light Detection and Ranging (LiDAR) remote sensing applications. Here, an automatic algorithm for pairwise coarse registration of TLS point clouds using 3D line features is proposed. First, the 3D line sets were extracted from the original pair of point clouds respectively, and two arbitrary lines from a point cloud were used to construct a 2-line base; Then, a pair of conjugate 2-line bases were identified from the source and target 3D line sets at a time, based on which a 3D rotation matrix together with its corresponding overlap between the pairwise 3D line sets was calculated; Third, a series of 3D rotation matrixes together with their overlaps were obtained using the traversing strategy to identify conjugate 2-line base pairs, and the 3D rotation matrix with the highest overlap was outputted as the final 3D rotation matrix, which was further used to compute the corresponding lines set from the pairwise 3D line sets; Next, based on the set of common perpendicular midpoints between the line correspondences, the 3D translation vector was computed; Finally, the 3D transformation matrix between pairwise point clouds was obtained by combining the 3D rotation matrix and the 3D translation vector. The proposed method was tested on three different TLS datasets, with experimental results demonstrating that the proposed algorithm could perform well on registering pairwise TLS point clouds. The rotation and translation errors of aligning the nine experimental pairwise point clouds were all less than 0.50° and 0.55m, respectively. This registration framework was also shown to be superior to state-of-the-art methods in terms of registration accuracy.

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Using 2-Lines Congruent Sets for Coarse Registration of Terrestrial Point Clouds in Urban Scenes

Cited by 17 publications

References 39 publications

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

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