线激光测量点云数据误差的预测与补偿

Prediction and Compensation of Point Cloud Data Error in Line Laser Measurement

针对目前航空叶片接触式测量中存在的效率低、表面易损伤等问题,设计并搭建了集航空叶片检测与加工于一体的线激光在机测量实验平台。在进行激光非接触测量中,点云数据容易出现误差。为了提高激光在机测量的精度,对在测量过程中的主要影响因素进行了探讨和分析。分别建立了基于径向基函数神经网络和支持向量回归机的误差预测模型,并对两种预测模型的性能进行了比较。提出自由曲面检测的误差补偿策略完成了点云数据的补偿和校正。最后以某型号航空叶片为例进行实验,实验结果表明,所提方法能将点云数据的精度提高39.86%,验证了误差补偿模型和补偿策略的可行性。

In this study,a line laser on-machine measurement experimental platform integrating aviation blade detection and processing is designed and built to solve the problems of low efficiency and easy surface damage during the contact measurement of aviation blades.In laser noncontact measurements,point cloud data are prone to errors.Herein,the main influencing factors involved in the measurement process are discussed and analyzed to improve the accuracy of laser in machine measurement.Furthermore,error prediction models based on radial basis function neural network and support vector regression are established and the performances of these two prediction models are compared.The error compensation strategy of free-form surface detection is used to complete the compensation and correction of point cloud data.Finally,taking a certain type of aviation blade as an example,the experimental results show that the proposed method can improve the accuracy of point cloud data by39.86%and verify the feasibility of the error compensation model and compensation strategy.