A Coarse-to-Fine Strip Mosaicing Model for Airborne Bathymetric LiDAR Data
A Coarse-to-Fine Strip Mosaicing Model for Airborne Bathymetric LiDAR Data
复制标题
机载测深激光雷达数据从粗到细的条带镶嵌模型
DOI：
10.1109/tgrs.2021.3050789
复制
发表时间：
2021-10-01
影响因子：
8.2
通讯作者：
Xu, Wenxue
中科院分区：
文献类型：
--
作者：
Ji, Xue;Yang, Bisheng;Xu, Wenxue
机载光探测和测距（LiDAR）测深（ALB）系统是无处不在的地形激光雷达测绘系统的扩展，最简单的特征是在地形系统的红外激光器上增加了一个绿色激光器。由于场景中点云密度低，物体单调，难以对ALB条带进行拼接。因此，现有的机载激光扫描条带拼接算法对ALB条带的拼接性能较差。本文提出了一种用于ALB的粗到细条带拼接模型。该框架速度快、效率高，可以处理大量的ALB数据。提出了一种改进的alpha形状算法，可以快速准确地确定条带的重叠区域。由于数据精度和空间特征不同，水域和陆地面积分别进行处理。设计了一种基于权重分布的水下区域粗到精配准模型。在非刚性迭代最近点（ICP）代价函数中加入拓扑约束项，防止异常值引起的过度变形。分别采用采用3L算法的隐式b样条曲面拟合算法和最小二乘趋势曲面拟合算法对重叠条带进行权值分配，解决了无控制或少控制的局限性。此外，构建了以法向量和曲率为特征的随机样本共识(RANSAC)-ICP配准模型。最后，与ICP的比较突出了该方法在灵活性和准确性方面的优势。均方根误差为0.12 m，最大误差为0.36 m。
The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is an extension of the ubiquitous topographic LiDAR mapping system and has been most simply characterized as adding a green laser to the infrared laser of topo systems. Due to the low point cloud density and monotonous objects in the scene, it is difficult to mosaicing the ALB strips. Therefore, the existing airborne laser scanning strip stitching algorithm has poor performance for ALB strips. In this article, a coarse-to-fine strip mosaicing model for ALB is proposed. The framework is fast and efficient and can handle large ALB data. An improved alpha shapes algorithm can fast and accurately determine the overlap region of strip is applied. Due to different data accuracy and spatial characteristics, the water area and land area are processed separately. A weight distribution-based coarse-to-fine registration model is designed for underwater areas. The topological constraint term is added to the nonrigid iterative closest point (ICP) cost function to prevent excessive deformation caused by outliers. The implicit B-spline surface fitting algorithm using the 3L algorithm and the least-squares trend surface fitting algorithm are applied separately to assign weights for overlapping strips to solve the limitation of no control or less control. Moreover, a random sample consensus (RANSAC)-ICP registration model characterized by the normal vector and curvature is constructed for land area. Finally, the comparisons with ICP highlight the superiority of the proposed approach in flexibility and accuracy. The root-mean-square error (RMSE) is 0.12 m and the maximum error is 0.36 m.