Point cloud data has sparsity， irregularity， and permutation invariance， and lacks topological information， which makes it difficult to extract features of point cloud. Therefore， a Siamese Adaptive Graph Convolution Algorithm （SAGCA） was proposed for point cloud classification and segmentation. Firstly， the topological relationships between irregular and sparse point cloud features were mined by constructing feature relationship graph. Then， the Siamese composition idea of sharing convolution learning weights was introduced to ensure the permutation invariance of point cloud data and make the topological relationship expression more accurate. Finally， SAGCA was combined with various deep learning networks for processing point cloud data by both global and local combination methods， thereby enhancing the feature extraction ability of the network. Comparison results with PointNet++ benchmark network of the classification， object part segmentation and scene semantic segmentation experiments on ScanObjectNN， ShapeNetPart and S3DIS datasets， respectively， show that， based on the same dataset and evaluation criteria， SAGCA has the class mean Accuracy （mAcc） of classification increased by 2.80 percentage points， the overall class average Intersection over Union （IoU） of part segmentation increased by 2.31 percentage points， and the class mean Intersection over Union （mIoU） of scene semantic segmentation increased by 2.40 percentage points， verifying that SAGCA can effectively enhance the feature extraction ability of the network and is suitable for multiple point cloud classification and segmentation tasks.

To solve the problem that the traditional hand-eye calibration algorithms have large solution errors due to the noise interference in the processes of vision sensor calibration and robot kinematics solution， a robot hand-eye calibration algorithm based on Covariance Matrix Adaptation Evolutionary Strategy （CMAES） was proposed. Firstly， the mathematical tool Dual Quaternion （DQ） was used to establish the objective functions and geometric constraints for both rotation and translation， and the solution model was simplified. Then， the penalty function method was used to transform the constrained problem into an unconstrained optimization problem. Finally， CMAES algorithm was used to approximate the global optimal solution of hand-eye calibration rotation and translation equations. An experimental platform of robot and camera measurement was built， and the proposed algorithm was compared with two-step Tsai algorithm， the nonlinear optimization algorithm INRIA， and the DQ algorithm. Experimental results show that the solution error and variance of the proposed algorithm are smaller than those of traditional algorithms for both rotation and translation. Compared with Tsai algorithm， the proposed algorithm has the rotation accuracy improved by 4.58%， and the translation accuracy improved by 10.54%. It can be seen that the proposed algorithm has better solution accuracy and stability in the actual hand-eye calibration process with noise interference.