高分辨率三维激光扫描数据的微小变形统计分析

地基三维激光扫描数据具有高精度、高空间分辨率的特点,能够改变传统的单点变形观测模式,使传统的点测量向“形测量”转化,因而大量的变形监测数据使得通过数学统计的方法探测变形量小于测量精度的微小变形成为可能。利用高分辨率三维激光扫描数据的微小变形统计分析方法,提出了基于反射值影像和BaySAC的拼接方法提高拼接精度;通过拟合方式提高整体分析精度,而影响拟合精度的主要因素为粗差,因此采用RanSAC算法剔除粗差,提高拟合的精度;然后利用格网化方法消弱偶然误差、修正拼接误差,最终得到变形量。采用建筑墙面及地铁隧道的点云数据进行了微小变形检测的实验,证明了该方法的有效性。

A Statistical and Analytical Method for Detecting Tiny Deformations in High Resolution 3D Laser Scanning Data

Terrestrial 3Dlaser scan data has high accuracy,high spatial resolution and can change thetraditional single-point observation mode.The traditional point measurement can be transformed intoshape measurement.Making it possible to detect tiny deformations in deformation monitoring datawhen the deformation is smaller than the measurement accuracy with mathematical and statisticalmethods.In this paper,we propose a statistical and analytical means to detect tiny deformations withhigh resolution 3Dlaser scanning data.We propose point cloud registration based on reflectance imageand BaySAC,and accurately registering it with ICP.Fitting can improve the overall analytical accura-cy,but the gross error is the main factor impacting fitting precision.We remove it with RANSAC al-gorithm.By eliminating random errors,revising registration errors,we ultimately arrive at the pre-cise deformation.The results from an experiment with building and subway tunnel data show that themethod is effective.