针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,...针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,将局部特征和全局特征融合,得到混合特征的特征矩阵。通过深度学习实现对应矩阵求解中相关参数的自动优化,最后利用加权奇异值分解(singular value decomposition,SVD)得到变换矩阵,完成配准。在ModelNet40数据集上的实验表明,和最远点采样相比,所提算法耗时减少45.36%;而配准结果和基于特征学习的鲁棒点匹配(robust point matching using learned features,RPM-Net)相比,平移矩阵均方误差降低5.67%,旋转矩阵均方误差降低13.1%。在自制点云数据上的实验,证实了算法在真实物体上配准的有效性。展开更多
For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing...For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.展开更多
文摘针对点云配准过程中,下采样时容易丢失关键点、影响配准精度的问题,本文提出一种基于特征融合和网络采样的配准方法,提高了配准的精度和速度。在PointNet分类网络基础上,引入小型注意力机制,设计一种基于深度学习网络的关键点提取方法,将局部特征和全局特征融合,得到混合特征的特征矩阵。通过深度学习实现对应矩阵求解中相关参数的自动优化,最后利用加权奇异值分解(singular value decomposition,SVD)得到变换矩阵,完成配准。在ModelNet40数据集上的实验表明,和最远点采样相比,所提算法耗时减少45.36%;而配准结果和基于特征学习的鲁棒点匹配(robust point matching using learned features,RPM-Net)相比,平移矩阵均方误差降低5.67%,旋转矩阵均方误差降低13.1%。在自制点云数据上的实验,证实了算法在真实物体上配准的有效性。
基金supported by National Basic Research Program of China(973 Program)(No.2012CB720000)National Natural Science Foundation of China(Nos.61225015 and 60974011)+3 种基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61321002)Beijing Municipal Natural Science Foundation(Nos.4102053 and 4101001)Beijing Natural Science Foundation(Nos.4132042)Beijing Higher Education Young Elite Teacher Project(No.YETP1212)
文摘For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.
