Hierarchical Optimization of 3D Point Cloud Registration

Liu, Huikai; Zhang, Yue; Xie, Hui; Li, Yan; Sun, Songlin

doi:10.3390/s20236999

Cited by 14 publications

(5 citation statements)

References 76 publications

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“…To validate the superior performance of the MSCS-ICP approach proposed herein, this section juxtaposes it against a variety of point cloud registration techniques. These include the multi-scale point cloud registration based on topological structure [13], MVG-ICP [12], LCP-4PCS [14], KSS-ICP [10], LPPF-ICP [11], and the method detailed in this study.…”

Section: Experimental Evaluation Of Stitching Accuracy For Standard S...mentioning

confidence: 99%

“…Yue et al [11] proposed a coarse-fine point cloud registration approach based on fast robust local point-pair features with ICP (LPPF-ICP), but due to the large amount of calculation for the point cloud, the matching time was long. Liu et al [12] proposed a hierarchical optimization method for 3D point cloud registration based on improved voxel filtering and multi-scale voxelized generalized-ICP (MVG-ICP), which successfully realized outlier filtering and down-sampling. However, the application of large translation disturbances and low overlap scene data to this method is not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Song,

Zhang,

Luo

et al. 2024

Meas. Sci. Technol.

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The effectiveness of point cloud registration critically determines 3D reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on Multi-view Spatial Coordinate System ICP to solve the problem of- 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3 to1.9m, our proposed method boasts a maximum error of just 0.069mm, an average absolute error of 0.039mm, and a root mean square error of 0.043mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.

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Section: Experimental Evaluation Of Stitching Accuracy For Standard S...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Song,

Zhang,

Luo

et al. 2024

Meas. Sci. Technol.

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show abstract

“…VGICP [20] estimates the voxel distribution by the distribution of each point in the aggregated voxels with the help of the GICP model. MVGICP [21] uses multiscale iteration to avoid the problem of improper voxel meshing in VGICP. Wang et al [22] proposed the FasterGICP algorithm to further improve the registration efficiency by designing a two-step point filter on top of the accept-reject sampling.…”

Section: Point Cloud Registrationmentioning

confidence: 99%

Accurate real-time SLAM based on two-step registration and multimodal loop detection

Zhang¹,

Zhang²,

Zhang³

2022

Meas. Sci. Technol.

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To improve the accuracy and efficiency of 3D LiDAR mapping, this paper improves the traditional point cloud registration and loop-detection methods and proposes a new scheme for accurate and real-time simultaneous localization and mapping systems. Cluster constraints are introduced into the front-end point cloud registration, and a two-step point cloud registration algorithm combining normal distribution transform and iterative closest point is proposed to speed up the point cloud registration. The effectiveness of this registration algorithm is fully validated in three sets of point cloud registration experiments. A loop detection process based on a height global descriptor for multimodal fusion is designed at the back end. The point cloud is encoded by this descriptor and fused with camera image information to generate a 1D operator to improve the search efficiency of loop closure frames. The proposed method is extensively evaluated on the KITTI data set and tested in playground periphery, fountain and parking lot environments. The results show that the accuracy of the proposed method outperforms the state-of-the-art LOAM and LeGO-LOAM in all three different scenarios.

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“…In addition, all points are involved in calculating the transformation matrix and, as such, calculation errors increase when local noise is too large or outliers are included [26]. Therefore, point clouds must be filtered before this registration process, and it has high requirements for filtering [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Registration Method Based on Histogram and Vector Operations

Zhang

Qiao

Xia³

et al. 2022

Electronics

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Point-pair registration in a real scene remains a challenging task, due to the complexity of solving three transformations (scale, rotation, and displacement) simultaneously, and the influence of noise and outliers. Aimed at this problem, a registration algorithm based on histogram and vector operations is proposed in this paper. This approach converts point-based operations into vector-based operations, thereby decomposing the registration process into three independent steps solving for scale transformation factors, rotation matrices, and displacement vectors, which reduces the complexity of the solution and avoids the effects of scaling in the other two processes. The influence of outliers on the global transformation matrix is simultaneously eliminated using a histogram-based approach. Algorithm performance was evaluated through a comparison with the most commonly used SVD method in a series of validation experiments, with results showing that our methodology was superior to SVD in the cases with scaling transformation or outliers.

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Hierarchical Optimization of 3D Point Cloud Registration

Cited by 14 publications

References 76 publications

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Accurate real-time SLAM based on two-step registration and multimodal loop detection

A Point Cloud Registration Method Based on Histogram and Vector Operations

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