摘要
采空区传统监测方法存在观测数据量少、无法或难以监测无人空区、不能定量观测空区垮落等缺点。采空区三维激光扫描仪可以有效、全面地扫描采空区的三维形态,但是由于矿山现场粉尘、水汽及仪器本身等的影响,获取的点云存在着各种噪声,并且由于现场地面可能会发生变形,前后两次扫描的点云并不能够完全重叠,这为点云的后续利用带来很大麻烦。为此,根据采空区点云的实际情况,提出了基于KD Tree的点云去噪方法和基于点云特征的配准方法,实验表明该方法可以有效地去除点云中存在的噪声及对点云进行配准,为后续的点云利用提供了数据基础。
The traditional cavity monitoring measures have many shortcomings, including of obtaining little of data, being difficult to monitor the unmanned cavity, and not calculating the volume of the cavity accurately. The three-dimensional （3D） laser scanner for cavity can scan the cavity to obtain the 3D point cloud data effectively and roundly, but it is trouble to use these point clouds which will exist a lot of noise that is formed by the dusty, moisture and the 3D laser scanner, and the first point cloud and the second point cloud will be misaligned because the ground may have the emergence of deformation. To solve these problems, this paper puts forward the point cloud denoising algorithm based on KD Tree and registration algorithm based on characteristics of point cloud. The experiments show that these algorithms are effective to remove the noise in the point cloud and realize the registration of point cloud, the point cloud will provide the data basic for mine to use in the future.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2013年第8期117-122,共6页
Acta Optica Sinica
基金
国家863计划(2008AA062101
2011AA060405)
国际合作项目(S2012ZR0401)
