论文标题
图理论方法可鲁棒的3D正常提取LIDAR数据
Graph-theoretical approach to robust 3D normal extraction of LiDAR data
论文作者
论文摘要
低维原始特征从LiDAR点云（例如平面）构成了大多数LiDAR数据处理任务的基础。 LIDAR数据分析中的一个主要挑战是雷达数据的不规则性质，迫使从业人员使用某种形式的网格化来正规化数据，或者使用三角形网格（例如三角形的不规则网络（TIN））。尽管使用LiDAR数据作为连接图的应用程序有少数应用程序，但仍缺乏利用图理论方法来进行LIDAR数据建模的原则治疗方法。在本文中，我们尝试通过利用图形方法来弥合这一差距，从而从激光点云中进行正常估计。我们在优化框架中提出了正常的估计问题，在该框架中，我们通过利用其最近的邻居找到每个LIDAR点的相应正常矢量，并同时根据点样本实现图平滑度假设。这是一个非线性约束的凸优化问题，然后可以使用投影的共轭梯度下降来解决，以产生独特的解决方案。为了增强我们的优化问题，我们还基于正态的点产物和点之间的欧几里得距离提供了不同的加权解决方案。为了评估我们提出的正常提取方法和加权策略的性能，我们首先对具有四个不同噪声水平和四个不同调谐参数的重复随机生成的数据集提供了详细的分析。最后，我们在大规模合成平面提取数据集上针对现有的最新方法基准了我们提出的方法。拟议方法的代码以及模拟和基准测试可在https://github.com/arpan-kusari/graph-plane-plane-extraction-simulation上获得。
Low dimensional primitive feature extraction from LiDAR point clouds (such as planes) forms the basis of majority of LiDAR data processing tasks. A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as triangulated irregular network (TIN). While there have been a handful applications using LiDAR data as a connected graph, a principled treatment of utilizing graph-theoretical approach for LiDAR data modelling is still lacking. In this paper, we try to bridge this gap by utilizing graphical approach for normal estimation from LiDAR point clouds. We formulate the normal estimation problem in an optimization framework, where we find the corresponding normal vector for each LiDAR point by utilizing its nearest neighbors and simultaneously enforcing a graph smoothness assumption based on point samples. This is a non-linear constrained convex optimization problem which can then be solved using projected conjugate gradient descent to yield an unique solution. As an enhancement to our optimization problem, we also provide different weighted solutions based on the dot product of the normals and Euclidean distance between the points. In order to assess the performance of our proposed normal extraction method and weighting strategies, we first provide a detailed analysis on repeated randomly generated datasets with four different noise levels and four different tuning parameters. Finally, we benchmark our proposed method against existing state-of-the-art approaches on a large scale synthetic plane extraction dataset. The code for the proposed approach along with the simulations and benchmarking is available at https://github.com/arpan-kusari/graph-plane-extraction-simulation.
