【24h】

HOLE-FILLING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD DATA

机译:空气填充算法空机激光乐队点云数据

获取原文
获取外文期刊封面目录资料

摘要

Due to the influence of the occlusion of objects or the complexity of the measured terrain in the scanning process of airborne lidar, the point cloud data inevitably appears holes after filtering and other processing. The incomplete data will inevitably have an impact on the quality of the reconstructed digital elevation model, so how to repair the incomplete point cloud data has become an urgent problem to be solved. To solve the problem of hole repair in point cloud data, a hole repair algorithm based on improved moving least square method is proposed in this paper by studying existing hole repair algorithms. Firstly, the algorithm extracts the boundary of the point cloud based on the triangular mesh model. Then we use k-nearest neighbor search to obtain the k-nearest neighbor points of the boundary point. Finally, according to the boundary point and its k-nearest neighbor point, the improved moving least squares method is used to fit the hole surface to realize the hole repair. Combined with C++ and MATLAB language, the feasibility of the algorithm is tested by specific application examples. The experimental results show that the algorithm can effectively repair the point cloud data holes, and the repairing precision is high. The filled hole area can be smoothly connected with the boundary.
机译:由于物体闭塞或测量地形的复杂性在机载LIDAR的扫描过程中的影响,在过滤和其他处理后,点云数据不可避免地出现孔。不完整的数据将不可避免地对重建数字高度模型的质量产生影响,因此如何修复不完整的点云数据已成为要解决的紧急问题。为了解决点云数据中的空穴修复问题,通过研究现有孔修复算法,提出了一种基于改进的移动最小二乘法的空穴修复算法。首先,算法基于三角网格模型提取点云的边界。然后我们使用k-incelte邻搜索获取边界点的k最接近邻点。最后,根据边界点及其k最近邻点,改进的移动最小二乘法用于装配孔表面以实现孔修复。结合C ++和MATLAB语言,算法的可行性由特定应用示例测试。实验结果表明,该算法可以有效地修复点云数据孔,修复精度高。填充的孔区域可以与边界平稳地连接。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号