{"title":"用于精确点云注册的具有倾斜分布的相干点漂移","authors":"Zhuoran Wang, Jianjun Yi, Lin Su, Yihan Pan","doi":"10.1016/j.cag.2024.103974","DOIUrl":null,"url":null,"abstract":"<div><p>Point cloud registration methods based on Gaussian Mixture Models (GMMs) exhibit high robustness. However, GMM cannot precisely depict point clouds, because the Gaussian distribution is spatially symmetric and local surfaces of point clouds are typically non-symmetric. In this paper, we propose a novel method for rigid point cloud registration, termed coherent point drift with Skewed Distribution (Skewed CPD). Our method employs an asymmetric distribution constructed from the local surface normals and curvature radii. Compared to the Gaussian distribution, this skewed distribution provides a more accurate spatial description of points on local surfaces. Additionally, we integrate an adaptive multiplier to the covariance, which reallocates the weight of the covariance for different components in the probabilistic mixture model. We employ the EM algorithm to address this maximum likelihood estimation (MLE) issue and leverage GPU acceleration. In the M-step, we adopt an unconstrained optimization technique rooted in a Lie group and Lie algebra to attain the optimal transformation. Experimental results indicate that our method outperforms state-of-the-art methods in both accuracy and robustness. Remarkably, even without loop closure detection, the cumulative error of our approach remains minimal.</p></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coherent point drift with Skewed Distribution for accurate point cloud registration\",\"authors\":\"Zhuoran Wang, Jianjun Yi, Lin Su, Yihan Pan\",\"doi\":\"10.1016/j.cag.2024.103974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Point cloud registration methods based on Gaussian Mixture Models (GMMs) exhibit high robustness. However, GMM cannot precisely depict point clouds, because the Gaussian distribution is spatially symmetric and local surfaces of point clouds are typically non-symmetric. In this paper, we propose a novel method for rigid point cloud registration, termed coherent point drift with Skewed Distribution (Skewed CPD). Our method employs an asymmetric distribution constructed from the local surface normals and curvature radii. Compared to the Gaussian distribution, this skewed distribution provides a more accurate spatial description of points on local surfaces. Additionally, we integrate an adaptive multiplier to the covariance, which reallocates the weight of the covariance for different components in the probabilistic mixture model. We employ the EM algorithm to address this maximum likelihood estimation (MLE) issue and leverage GPU acceleration. In the M-step, we adopt an unconstrained optimization technique rooted in a Lie group and Lie algebra to attain the optimal transformation. Experimental results indicate that our method outperforms state-of-the-art methods in both accuracy and robustness. Remarkably, even without loop closure detection, the cumulative error of our approach remains minimal.</p></div>\",\"PeriodicalId\":50628,\"journal\":{\"name\":\"Computers & Graphics-Uk\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Graphics-Uk\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0097849324001092\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Graphics-Uk","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0097849324001092","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
摘要
基于高斯混合模型(GMM)的点云注册方法具有很高的鲁棒性。然而,GMM 无法精确描绘点云,因为高斯分布在空间上是对称的,而点云的局部表面通常是不对称的。在本文中,我们提出了一种用于刚性点云注册的新方法,即倾斜分布相干点漂移(Skewed CPD)。我们的方法采用了由局部表面法线和曲率半径构建的非对称分布。与高斯分布相比,这种倾斜分布能对局部表面上的点进行更精确的空间描述。此外,我们还在协方差中加入了自适应乘数,重新分配概率混合模型中不同成分的协方差权重。我们采用 EM 算法来解决最大似然估计 (MLE) 问题,并利用 GPU 加速。在 M 步中,我们采用了植根于李群和李代数的无约束优化技术,以实现最优变换。实验结果表明,我们的方法在准确性和鲁棒性方面都优于最先进的方法。值得注意的是,即使没有闭环检测,我们方法的累积误差仍然很小。
Coherent point drift with Skewed Distribution for accurate point cloud registration
Point cloud registration methods based on Gaussian Mixture Models (GMMs) exhibit high robustness. However, GMM cannot precisely depict point clouds, because the Gaussian distribution is spatially symmetric and local surfaces of point clouds are typically non-symmetric. In this paper, we propose a novel method for rigid point cloud registration, termed coherent point drift with Skewed Distribution (Skewed CPD). Our method employs an asymmetric distribution constructed from the local surface normals and curvature radii. Compared to the Gaussian distribution, this skewed distribution provides a more accurate spatial description of points on local surfaces. Additionally, we integrate an adaptive multiplier to the covariance, which reallocates the weight of the covariance for different components in the probabilistic mixture model. We employ the EM algorithm to address this maximum likelihood estimation (MLE) issue and leverage GPU acceleration. In the M-step, we adopt an unconstrained optimization technique rooted in a Lie group and Lie algebra to attain the optimal transformation. Experimental results indicate that our method outperforms state-of-the-art methods in both accuracy and robustness. Remarkably, even without loop closure detection, the cumulative error of our approach remains minimal.
期刊介绍:
Computers & Graphics is dedicated to disseminate information on research and applications of computer graphics (CG) techniques. The journal encourages articles on:
1. Research and applications of interactive computer graphics. We are particularly interested in novel interaction techniques and applications of CG to problem domains.
2. State-of-the-art papers on late-breaking, cutting-edge research on CG.
3. Information on innovative uses of graphics principles and technologies.
4. Tutorial papers on both teaching CG principles and innovative uses of CG in education.