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。
Abstract
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
Key words
point cloud data /
line and surface fitting /
improved random sampling consensus (M-RANSAC) /
building status survey
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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)
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