改进随机抽样一致性算法在建筑物现状测量中的应用  

Application of Improved Random Sample Consensus inBuilding Status Survey

作　　者：黎建洲 韩贤权[1,2,3] 万鹏 LI Jian-zhou;HAN Xian-quan;WAN Peng(Engineering Safety and Disaster Prevention Department,Changjiang River Scientific Research Institute,Wuhan 430010,China;Research Center on Water Engineering Safety and Disaster Prevention of Ministry of Water Resources,Wuhan 430010,China;Research Center on National Dam Safety Engineering Technology,Wuhan 430010,China)

机构地区：[1]长江科学院工程安全与灾害防治研究所,武汉430010 [2]水利部水工程安全与病害防治工程技术研究中心,武汉430010 [3]国家大坝安全工程技术研究中心,武汉430010

出　　处：《长江科学院院报》2023年第8期152-156,176,共6页Journal of Changjiang River Scientific Research Institute

基　　金：中央级公益性科研院所基本科研业务费项目(CKSF2021449/GC);国家自然科学基金项目(42001374,42271447)。

摘　　要：作为点云数据处理的关键步骤,线面拟合的结果直接影响后续数据处理的精度。针对传统RANSAC算法在建筑物平面拟合过程中存在的效率及精度方面的问题,引入一种顺序法向量检测机制,通过增加拟合模型的角度约束条件,提出了一种改进随机抽样一致性算法(M-RANSAC)进行平面和边缘探测。通过与常规全站仪测量方法以及传统RANSAC算法进行结果对比,验证了本方法的可行性和有效性,并在珠江三角洲水资源配置工程沿线建筑物现状测量中得到了成功应用。The quality of line and surface fitting results has a direct impact on the accuracy of subsequent data processing,making it a crucial step in point cloud data processing.To address the limitations of traditional RANSAC(Random Sample Consensus)algorithms in the effectiveness and accuracy of plane fitting for buildings,we introduce a sequential normal vector detection mechanism and propose an improved algorithm called M-RANSAC.The M-RANSAC algorithm enhances plane and edge detection by introducing increased angle constraints to the fitting model.To validate the feasibility and effectiveness of this method,a comparison was made with the conventional total station measurement method and the traditional RANSAC algorithm.The results confirmed the superiority of the proposed approach.Furthermore,the method was successfully applied in the current situation survey of buildings along the Pearl River Delta water resources allocation project.In conclusion,the M-RANSAC algorithm offers improved accuracy and efficiency in line and surface fitting.

关 键 词：点云数据 线面拟合 改进随机抽样一致性(M-RANSAC)算法 建筑物现状测量 

分 类 号：P224[天文地球—大地测量学与测量工程]