摘要：【中文】本发明提供一种点邻域尺度差异描述的点云配准方法，通过对源点云和目标点云中离散点进行最小二乘曲面拟合得到局部曲面，求出曲面的形状指数SI，即该离散点的形状指数，选取形状指数在邻域内最大或最小且满足阈值的点作为点云的关键点；进行特征描述符构造，计算关键点在不同邻域半径下的特征归一化向量差值和法向量夹角差值组合成点领域尺度差异描述符；根据特征描述符的相似程度找出对应点并使用二重筛选和基于全局距离的最优查找算法分别滤除错误点对和估计对应关系。本发明得到的关键点具有很好的代表性和区别性，对点云分布密度差异较大或存在噪声点的情况效果明显，计算简单，提高点云配准速度和精度，具有很好的抗干扰能力。 【EN】The invention provides a point cloud registration method for describing point neighborhood scale difference, which comprises the steps of performing least square surface fitting on discrete points in source point cloud and target point cloud to obtain a local curved surface, solving the shape index SI of the curved surface, namely the shape index of the discrete points, and selecting the point with the shape index which is the largest or the smallest in a neighborhood and meets a threshold value as a key point of the point cloud; constructing a feature descriptor, and calculating a feature normalization vector difference value and a normal vector included angle difference value of the key point under different neighborhood radiuses to form a point domain scale difference descriptor; and finding out corresponding points according to the similarity of the feature descriptors, and respectively filtering error point pairs and estimating corresponding relations by using a double-screening and global distance-based optimal search algorithm. The key points obtained by the method have good representativeness and distinctiveness, have obvious effect on the condition that the distribution density difference of point clouds is large or noise points exist, are simple to calculate, improve the point cloud registration speed and precision and have good anti-interference capability.

摘要：【中文】本发明公开了基于深度增强学习的图信号节点采样方法，属于机器学习领域。该方法基于经典分立空间增强学习算法Deep Q Learning方法，把图中所有的信号节点作为增强学习中的动作空间，增强学习智体通过学习采取合适的节点来最大化地保留原图所包含的信息。我们独创性地设计了智体所运行的环境，在这个环境中智体通过采取动作来获得回报，不断的训练与提升其采样策略。该方法不需要大量的有标签数据，使用神经网络来处理大量的图数据，使用增强学习算法来自动化这一流程。实现对部分节点的精准筛选。训练好的智体可以在环境中自动根据图的特征选取合适的节点进行筛选，只要实际应用问题可以抽象为信号图，而且全程自动化采样，没有任何附加成本和人力参与。 【EN】The invention discloses a graph signal node sampling method based on deep reinforcement learning, and belongs to the field of machine learning. The method is based on a classic discrete space reinforcement Learning algorithm Deep Q Learning method, all signal nodes in a graph are used as action spaces in reinforcement Learning, and the reinforcement Learning intelligence adopts proper nodes through Learning to maximally retain information contained in an original graph. The environment in which the intelligence operates is originally designed, and in the environment, the intelligence obtains return by taking action, and the sampling strategy is continuously trained and promoted. The method does not require a large amount of labeled data, uses neural networks to process a large amount of graph data, and uses an enhanced learning algorithm to automate this process. And realizing accurate screening of partial nodes. The trained wisdom can automatically select proper nodes to screen according to the characteristics of the graph in the environment, as long as the practical application problem can be abstracted into a signal graph, and the whole process is automatic in sampling without any additional cost and manpower participation.