An improved point cloud registration method based on the point-by-point forward method
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摘要:
点云配准是获取三维点云模型空间姿态的关键步骤，为了进一步提高点云配准的效率和准确性，提出了一种基于逐点前进法特征点提取的改进型点云配准方法。首先，利用逐点前进法快速提取点云特征点，在保留点云模型特征的同时大幅精简点云数量。然后，通过使用法向量约束改进的KN-4PCS算法进行粗配准，以实现源点云与目标点云的初步配准。最后，使用双向Kd-tree优化的LM-ICP算法完成精配准。实验结果表明，本文方法具有较高的精度和效率，同时具有较好的鲁棒性，在斯坦福大学开放点云数据配准实验中，其平均误差较SAC-IA+ICP算法减少了约70.2％，较NDT+ICP算法减少了约49.6％，配准耗时分别减少约86.2%和81.9%，同时在引入不同程度的高斯噪声后仍能保持较高的精度和较低的耗时。在真实室内物体点云配准实验中，其平均配准误差为0.0742 mm，算法耗时平均为0.572 s。通过斯坦福开放数据与真实室内场景物体点云数据对比分析表明，本方法能够有效地提高点云配准的效率、准确性和鲁棒性，为基于点云的室内目标识别与位姿估计奠定了良好的基础。
Abstract:To improve both the efficiency and accuracy of point cloud registration, this study proposed an improved method based on point-by-point advance feature point extraction. Firstly, the point-by-point advance method extracts point cloud feature points rapidly, and greatly reduces the number of point clouds, while retaining the characteristics of the point cloud model. The KN-4PCS algorithm, using normal vector constraints, conducts a preliminary registration of the source and target point cloud. Finally, the fine registration is achieved with the two-way Kd-tree optimized LM-ICP algorithm. In the open point cloud data registration experiment of Stanford University, the average error is reduced by about 70.2% compared with the SAC-IA+ICP algorithm, and the registration time is reduced by about 86.2% and 81.9%, respectively. The algorithm maintains high accuracy and low time consumption even with varying degrees of Gaussian noise. In the point cloud registration experiment of indoor objects, the average registration error was measured to be 0.0742 mm with an average algorithm time of 0.572 s. The comparison and analysis of Stanford open data and real indoor scene object point cloud data shows that this method can effectively improve the efficiency, accuracy, and robustness of point cloud registration. Furthermore, this study establishes a strong foundation for indoor target recognition and pose estimation through the point cloud.
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Key words:
- point cloud registration /
- KN-4PCS /
- bidirectional Kd-tree /
- LM-ICP
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图 7 高斯噪声σ=0.003时斯坦福点云的配准结果
Figure 7. Registration results of Stanford point cloud data at gaussian noise of σ= 0.003
图 8 不同高斯噪声时各算法的配准误差
Figure 8. Registration error of each algorithm with different gaussian noise
图 9 不同高斯噪声时各算法的配准耗时
Figure 9. The registration time consumed by each algorithm under different gaussian noise
图 10 真实室内场景物体点云配准结果
Figure 10. Object point cloud data registration results in indoor scene
表 1 深度相机参数
Table 1. Deep camera parameters
参数名称 数值 工作范围 0.6-8 w 深度 精度 lm: ±3 mm 视场角（FOV) H58.4 × V45.5° 分辨率@帧率 640 × 480@30 fps 视场角（FOV) H66.1° × 740.2° RGB 分辨率@帧率 640 × 480@30 fps UVC 支持 
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表 2 斯坦福点云配准定量分析结果
Table 2. Quantitative analysis results of Stanford point cloud data registration
Model SAC-IA+ICP NDT+ICP Ours RMSE/mm Time/s RMSE/mm Time/s RMSE/mm Time/s Armadillo 0.0288 3.21 0.0168 2.19 0.00730 0.437 Dragon 0.0363 3.77 0.0218 3.25 0.0125 0.528
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表 3 真实室内场景物体点云配准定量分析结果
Table 3. Quantitative analysis results of object point cloud data registration in indoor scene
模型 源点云数/个 源点云特征点数/个 目标点云数/个 目标点云特征点数/个 RMSE/mm 平均误差/mm 耗时/s 平均耗时/s Chair 8549 3702 10431 4847 0.0686 0.544 Kettle 3713 1359 3879 1449 0.0633 0.0742 0.487 0.572 Mannequin 45292 16098 46507 16462 0.0907 0.686
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