激光点云匹配是影响激光SLAM系统精度和效率的关键因素.传统激光SLAM算法无法区分场景结构,且在非结构化场景下由于特征提取不佳而出现性能退化.为此,提出一种联合CPD(coherent point drift)面向复杂场景的自适应激光SLAM算法CPD-LOAM....激光点云匹配是影响激光SLAM系统精度和效率的关键因素.传统激光SLAM算法无法区分场景结构,且在非结构化场景下由于特征提取不佳而出现性能退化.为此,提出一种联合CPD(coherent point drift)面向复杂场景的自适应激光SLAM算法CPD-LOAM.该算法提出一种基于预判和验证相结合的场景结构辨识方法,首先引入场景特征变量对场景结构进行初步判断,然后从几何特征角度通过表面曲率对其进行验证,增强对场景结构辨识的准确性.此外,在非结构化场景下添加CPD算法进行点云预配准,进而利用ICP算法进行再配准,解决该场景下的特征退化问题,从而提高点云配准的精度和效率.实验结果表明,提出的场景特征变量以及表面曲率可以根据设置的阈值有效地区分场景结构,在公开数据集KITTI上的验证结果显示,CPD-LOAM较LOAM算法定位误差降低了84.47%,相较于LeGO-LOAM与LIO-SAM算法定位精度也分别提升了55.88%和30.52%,且具有更高的效率和鲁棒性.展开更多
散射中心匹配是当前散射中心用于SAR图像目标识别的一个主要技术途径。散射中心匹配的难点在于散射中心特征存在的误差和缺失。Coherent Point Drift(CPD)方法从概率密度估计的角度解决点模式匹配问题,能够较好地考虑散射中心的误差和...散射中心匹配是当前散射中心用于SAR图像目标识别的一个主要技术途径。散射中心匹配的难点在于散射中心特征存在的误差和缺失。Coherent Point Drift(CPD)方法从概率密度估计的角度解决点模式匹配问题,能够较好地考虑散射中心的误差和缺失。本文将CPD方法用于散射中心匹配,并在此基础上引入车辆目标SAR图像方位角估计先验信息和散射中心属性信息,以提高散射中心匹配的准确性和稳健性。MSTAR数据实验说明了该方法的有效性。展开更多
Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how t...Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.展开更多
针对面部点云配准中头发颜色与面部肤色存在差异,导致配准效率降低的问题,提出一种改进的相干点漂移(Coherent Point Drift,CPD)算法。该算法先提取面部点云的距离和颜色信息,将颜色信息转化为亮度信息,再仿照双侧滤波算法计算对应的亮...针对面部点云配准中头发颜色与面部肤色存在差异,导致配准效率降低的问题,提出一种改进的相干点漂移(Coherent Point Drift,CPD)算法。该算法先提取面部点云的距离和颜色信息,将颜色信息转化为亮度信息,再仿照双侧滤波算法计算对应的亮度权值系数。将亮度权值系数与距离权值系数相融合,得到新的加权系数,有效降低头发区域点的权重,以提高算法的配准精度与效率。最后,将改进的CPD算法应用于面部点云配准。实验结果表明,该算法能够将两点云的距离标准差平均降低66.01%,运算时间平均缩短18.57%,显著提高了面部点云配准效果。展开更多
The Coherent Point Drift (CPD) algorithm which based on Gauss Mixture Model is a robust point set registration algorithm. However, the selection of robustness weight which used to describe the noise may directly affec...The Coherent Point Drift (CPD) algorithm which based on Gauss Mixture Model is a robust point set registration algorithm. However, the selection of robustness weight which used to describe the noise may directly affect the point set registration efficiency. For resolving the problem, this paper presents a CPD registration algorithm which based on distance threshold constraint. Before the point set registration, the inaccurate template point set by resampling become the initial point set of point set matching, in order to eliminate some points that the distance to target point set is too close and too far in the inaccurate template point set, and set the weights of robustness as . In the simulation experiments, we make two group experiments: the first group is the registration of the inaccurate template point set and the accurate target point set, while the second group is the registration of the accurate template point set and the accurate target point set. The results of comparison show that our method can solve the problem of selection for the weight. And it improves the speed and precision of the original CPD registration.展开更多
文摘激光点云匹配是影响激光SLAM系统精度和效率的关键因素.传统激光SLAM算法无法区分场景结构,且在非结构化场景下由于特征提取不佳而出现性能退化.为此,提出一种联合CPD(coherent point drift)面向复杂场景的自适应激光SLAM算法CPD-LOAM.该算法提出一种基于预判和验证相结合的场景结构辨识方法,首先引入场景特征变量对场景结构进行初步判断,然后从几何特征角度通过表面曲率对其进行验证,增强对场景结构辨识的准确性.此外,在非结构化场景下添加CPD算法进行点云预配准,进而利用ICP算法进行再配准,解决该场景下的特征退化问题,从而提高点云配准的精度和效率.实验结果表明,提出的场景特征变量以及表面曲率可以根据设置的阈值有效地区分场景结构,在公开数据集KITTI上的验证结果显示,CPD-LOAM较LOAM算法定位误差降低了84.47%,相较于LeGO-LOAM与LIO-SAM算法定位精度也分别提升了55.88%和30.52%,且具有更高的效率和鲁棒性.
文摘散射中心匹配是当前散射中心用于SAR图像目标识别的一个主要技术途径。散射中心匹配的难点在于散射中心特征存在的误差和缺失。Coherent Point Drift(CPD)方法从概率密度估计的角度解决点模式匹配问题,能够较好地考虑散射中心的误差和缺失。本文将CPD方法用于散射中心匹配,并在此基础上引入车辆目标SAR图像方位角估计先验信息和散射中心属性信息,以提高散射中心匹配的准确性和稳健性。MSTAR数据实验说明了该方法的有效性。
基金supported by Natural Science Foundation of Anhui Province (2108085MF210,1908085MF187)Key Natural Science Fund of Department of Eduction of Anhui Province (KJ2021A0042)Natural Social Science Foundation of China (19BTY091).
文摘Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.
文摘针对面部点云配准中头发颜色与面部肤色存在差异,导致配准效率降低的问题,提出一种改进的相干点漂移(Coherent Point Drift,CPD)算法。该算法先提取面部点云的距离和颜色信息,将颜色信息转化为亮度信息,再仿照双侧滤波算法计算对应的亮度权值系数。将亮度权值系数与距离权值系数相融合,得到新的加权系数,有效降低头发区域点的权重,以提高算法的配准精度与效率。最后,将改进的CPD算法应用于面部点云配准。实验结果表明,该算法能够将两点云的距离标准差平均降低66.01%,运算时间平均缩短18.57%,显著提高了面部点云配准效果。
文摘The Coherent Point Drift (CPD) algorithm which based on Gauss Mixture Model is a robust point set registration algorithm. However, the selection of robustness weight which used to describe the noise may directly affect the point set registration efficiency. For resolving the problem, this paper presents a CPD registration algorithm which based on distance threshold constraint. Before the point set registration, the inaccurate template point set by resampling become the initial point set of point set matching, in order to eliminate some points that the distance to target point set is too close and too far in the inaccurate template point set, and set the weights of robustness as . In the simulation experiments, we make two group experiments: the first group is the registration of the inaccurate template point set and the accurate target point set, while the second group is the registration of the accurate template point set and the accurate target point set. The results of comparison show that our method can solve the problem of selection for the weight. And it improves the speed and precision of the original CPD registration.
