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復雜采空區激光多點探測及點云拼接與精簡

Point cloud merging and compression of complicated goaf using multi-point laser-scan

  • 摘要: 針對復雜采空區激光探測中存在探測“盲區”和點云數據分布不均的問題,研究激光多點掃描和點云數據拼接與精簡方法.通過多點探測避免了單次探測“盲區”,加密了數據稀疏區.提出了基于公共坐標和最小二乘法的靶標矩陣轉換方法,實現了多點探測點云的拼接.統計了點云密集區的分布規律;對密集散亂點云,提出了沿y軸方向分層剖分,層內數據以xz坐標極值分區,區內每點以x值排序后依步長篩選的精簡算法.大型貫通采空區驗證表明:基于最小二乘法的拼接算法最優,誤差范圍在0.1 mm左右;數據精簡率為15%-25%,確保了邊界三維信息的完整性.

     

    Abstract: In view of the problems of ‘blind spots’ in complicated goaf detecting by using laser scanning and point cloud density distribution inhomogeneity, this article introduced multi-point laser scan and point cloud merging and compression. Multi-point scan in complicated goaf avoided ‘blind spots’ and densified sparse point cloud regions. The merging algorithm of point cloud data was put forward based on a common coordinate system and the least-squares principle to solve the target transformation matrix. After the distribution rule of point cloud concentration areas was analyzed, the scattered point cloud compression algorithm was proposed, in which the point cloud was divided into portions along the y direction firstly, then intralayer data were divided by the extreme values of x and z, and each point was sorted on the x value and screened on step k. Error analysis of an instance of large versed goaf shows that the merging algorithm based on the least-squares principle will achieve high precision with an error range of about 0.1 mm. The compression algorithm can achieve a compression proportion of 15% to 25% and ensure the integrity of 3D boundary information at the same time.

     

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