基于随机抽样一致算法的误匹配标志点校正方法

Mismatching Marked Points Correction Method Based on Random Sample Consensus Algorithm

大型零件三维测量过程中常需要粘贴较多的标志点来进行自动拼接。由于人工粘贴标志点的随机性与噪声等因素的影响,标志点自动匹配时极易产生误匹配标志点,影响了多次测量时点云数据自动拼合的稳定性。针对此问题,在实现标志点自动匹配的基础上引入随机抽样一致(RANSAC)算法去除误匹配标志点。该方法根据选定好的目标模型和相关评判准则,将所有的匹配标志点分为内点和外点,利用内点计算出当前最佳目标模型参数,经过一定次数的随机采样后计算出最终的最佳目标模型参数,从而有效地去除大型零件点云数据自动拼合过程中出现的距离误匹配标志点和噪声误匹配标志点。模拟实验和拼接实例表明该方法是可行的,能有效地提高大型零件点云数据自动拼合的稳定性。

It is often needed to paste many marked points to realize auto-registration in the process of large parts three-dimensional （3D） measurement. Because of the randomness of artificially pasted marked points and noise factors, mismatching marked points often exist in auto-matching, which affect the stability of point-clouds auto- registration for repeated measurements. For this problem, a method is presented which uses random sample consensus （RANSAC） algorithm to remove the mismatching marked points based on the auto-matching of marked points. The method divides all matching marked points into inner points and outer points according to the selected target model and related criteria, calculates the current optimum target model parameters using the inner points and finally calculates the best parameters after a certain times of random sampling. It effectively removes the distance and noise mismatching marked points which are generated in the process of point-clouds auto-registration of large parts. Simulation experiment and registration examples demonstrate that the method is practicable and improves the stability of point-clouds auto-registration effectively.