The data of three-dimensional point cloud obtained by laser sensor scanning is inevitably mixed with noises and outliers,resulting in a decrease in the fitting accuracy of the point cloud plane. In order to solve this problem,this paper proposes a method that combines M-estimate Sample Consensus (MSAC) algorithm and principal component analysis (PCA) method to fit the point cloud plane. Firstly,via this method,the MSAC algorithm is used to remove the abnormal points for the point cloud data,and the ideal point cloud plane is obtained. Then,the PCA method is used to fit the retained point cloud data to obtain more accurate point cloud plane parameters.Using the battery tray as the test object,a 3D line laser profile sensor is used to scan the test object and transmit the point cloud data to a computer for processing.Through experiments with the simulation data and battery tray point cloud data,it is found that compared with the method of random sample consensus (RANSAC) combined with PCA and least square median (LMedS) combined with PCA,the proposed method can significantly reduce the influence of outliers on point cloud plane fitting and obtain more accurate plane fitting parameters when the time consumption is approaching.When plane fitting is performed on the two parts of the battery tray point cloud after filtering,it can be found that the standard deviation of the proposed method is reduced by 28.6％ and 22.5％,24.0％ and 29.0％,respectively,compared with the other two methods.Thus,this method has strong plane fitting accuracy and practicability.
point cloud dataabnormal pointsplane fittingM-estimationprincipal component analysis method
维普
万方数据
