1.湖南科技大学机电工程学院 湘潭 411201; 2.难加工材料高效精密加工湖南省重点实验室 湘潭 411201; 3.长沙视浪科技有限公司 长沙 410006; 4.韶关学院智能工程学院 韶关 512005
TP391
湘潭市科技计划项目(ZD-YB20211003)、韶关学院博士引进基金（440-9900064607）项目资助
Yang Shaozhou
1.School of Mechatronics Engineering, Hunan University of Science and Technology，Xiangtan 411201, China； 2.Hunan Provincial Key Laboratory of High Efficient and Precision Machining of Difficult-to-Process Material， Xiangtan 411201, China；3.Changsha Shi-lang Technology Co., Ltd.，Changsha 410006, China； 4.Collefe of Intelligent Engineering,Shaoguan University，Shaoguan 512005, ChinaLong Dongping
1.School of Mechatronics Engineering, Hunan University of Science and Technology，Xiangtan 411201, China； 2.Hunan Provincial Key Laboratory of High Efficient and Precision Machining of Difficult-to-Process Material， Xiangtan 411201, China；3.Changsha Shi-lang Technology Co., Ltd.，Changsha 410006, China； 4.Collefe of Intelligent Engineering,Shaoguan University，Shaoguan 512005, ChinaChen Jiyao
1.School of Mechatronics Engineering, Hunan University of Science and Technology，Xiangtan 411201, China； 2.Hunan Provincial Key Laboratory of High Efficient and Precision Machining of Difficult-to-Process Material， Xiangtan 411201, China；3.Changsha Shi-lang Technology Co., Ltd.，Changsha 410006, China； 4.Collefe of Intelligent Engineering,Shaoguan University，Shaoguan 512005, China1.School of Mechatronics Engineering, Hunan University of Science and Technology，Xiangtan 411201, China; 2.Hunan Provincial Key Laboratory of High Efficient and Precision Machining of Difficult-to-Process Material， Xiangtan 411201, China;3.Changsha Shi-lang Technology Co., Ltd.，Changsha 410006, China; 4.Collefe of Intelligent Engineering,Shaoguan University，Shaoguan 512005, China
通过激光传感器获取的三维点云难免混入噪声和异常点，导致点云平面的拟合精度降低。为解决该问题，本文提出了一种结合M估计样本一致性(MSAC)算法和主成分分析(PCA)法拟合点云平面的方法。该方法首先通过MSAC算法去除点云数据中的异常点，获得较为理想的点云平面，然后使用PCA方法对保留的点云数据进行平面拟合，以获取更加精确的点云平面参数。使用电池托盘作为被测物，应用3D线激光轮廓传感器扫描被测物并将点云数据传输到计算机进行处理。通过设定的仿真数据和电池托盘点云数据进行实验，发现本文方法与随机采样一致性(RANSAC)结合PCA、最小平方中值(LMedS)结合PCA的方法相比，在耗时接近的情况下，能够显著降低异常点对点云平面拟合的影响，获得更精确的平面拟合参数。对两个部分的电池托盘点云滤波处理后进行平面拟合时，能够发现本文方法与其他两种方法相比，标准差分别降低了28.6%和22.5%%、24.0%和29.0%，该方法具有较高的平面拟合精度和实用性。
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.
杨少舟,龙东平,陈继尧,吴士旭,徐先懂.基于M估计算法的三维点云平面拟合方法研究[J].电子测量技术,2024,47(5):70-76
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