A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds

Xiong, Fengguang; Biao, Dong; Wang, Huo; Pang, Mingjun; Kuang, Liqun

doi:10.1109/access.2020.2995369

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…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]. Therefore, the ICP algorithm requires data to complete rough alignment in advance to avoid acquiring a less qualified initial value from the local optimization process.…”

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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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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“…To improve the accuracy and real-time performance of a 3D vision-based robotic arm to complete disorderly grasping, this paper incorporates some better point cloud processing algorithms to improve the algorithm processing, improve the Euclidean clustering algorithm, and fuse point cloud edge information to complete point cloud segmentation [3] to improve the accuracy of point cloud segmentation. A new set of metazoans is introduced into the FPFH to increase the accuracy of point cloud feature recognition and obtain point cloud data with high information content [4][5][6] , and then the GPD algorithm is used to obtain the 6DOF gripping coordinates of the robot arm. The gripping coordinates are then used to obtain the 6DOF gripping coordinates of the robot arm [7] .…”

Section: Introductionmentioning

confidence: 99%

Design of an Unordered Gripping System Based on 3D Vision

Chen,

Bai,

et al. 2023

J. Phys.: Conf. Ser.

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For the current mainstream 2D vision-guided robotic arm gripping system in industrial production, the vision part is seriously affected by the lighting environment, camera imaging distance, and other factors. It cannot provide more comprehensive 6DOF position information on the target workpiece and complete the gripping of mutually scattered and stacked workpieces. This paper designs a disordered grasping system based on the 3D vision to solve the above problems. In addition, the traditional Euclidean clustering algorithm and fast point feature histogram algorithm are improved to enhance the segmentation and feature recognition efficiency of the point cloud. The accuracy of the system is 94.3 % without stacking and 88.2 % with stacking, which is 5.2 % higher than the average accuracy of the system before the algorithm improvement and more than 2s higher than the time. The accuracy and speed of the system meet the requirements of general industrial production for a robotic arm grasping system, which has certain practical application value.

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“…Li et al [5] fused the SAC-IA algorithm with the improved ICP algorithm, which improved the alignment time but the accuracy of the alignment was not much different from the original ICP; Wang et al [6] proposed a point cloud alignment method based on Normal Distribution Transform (NDT) combined with ICP, which had an advantage in alignment time compared to the SAC-IA algorithm but the accuracy of the alignment was slightly worse;3.In other aspects, Drost et al [7] proposed a PPF point pair feature algorithm. Later, many researchers have improved the PPF algorithm [8][9][10][11][12], and the improvement effect of Reference [9] is the most obvious. The algorithm significantly improves the ability of point-to-point features to cope with noise by spreading features into the neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Research on point cloud matching algorithm based on point pair feature and improved ICP

qi,

ge,

sun

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds

Cited by 15 publications

References 39 publications

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

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

Design of an Unordered Gripping System Based on 3D Vision

Research on point cloud matching algorithm based on point pair feature and improved ICP

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