Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
Ruifang Sun,Enzhong Zhang,Deqiang Mu,Shijun Ji,Ziqiang Zhang,Hongwei Liu,Zheng Fu +6 more
TL;DR: Wang et al. as mentioned in this paper proposed a new registration method that combines the curvature feature and fast point feature histogram (FPFH) in the precise registration stage, and the algorithm is analyzed experimentally.
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Abstract: In order to solve the problem of the traditional iterative closest point algorithm (ICPA), which requires a high initial position of point cloud and improves the speed and accuracy of point cloud registration, a new registration method is proposed in this paper. Firstly, the rough registration method is optimized. As for the extraction of the feature points, a new method of feature point extraction is adopted, which can better keep the features of the original point cloud. At the same time, the traditional point cloud filtering method is improved, and a voxel idea is introduced to filter the point cloud. The edge length data of the voxels is determined by the density, and the experimentally verified noise removal rates for the 3D cloud data are 95.3%, 98.6%, and 93.5%, respectively. Secondly, a precise registration method that combines the curvature feature and fast point feature histogram (FPFH) is proposed in the precise registration stage, and the algorithm is analyzed experimentally. Finally, the two point cloud data sets Stanford bunny and free-form surface are analyzed and verified, and it is concluded that this method can reduce the error by about 40.16% and 36.27%, respectively, and improve the iteration times by about 42.9% and 37.14%, respectively.
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References
A method for registration of 3-D shapes
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TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
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Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
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Sofien Bouaziz,Andrea Tagliasacchi,Mark Pauly +2 more
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TL;DR: This work proposes a new formulation of the Iterative Closest Point algorithm that retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data.
NICP: Dense normal based point cloud registration
Jacopo Serafin,Giorgio Grisetti +1 more
- 17 Dec 2015
TL;DR: A novel on-line method to recursively align point clouds by considering each point together with the local features of the surface (normal and curvature) that minimizes an error metric depending on these surface characteristics.
Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets
TL;DR: The main contributions of the proposed method are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.
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