Real-time Processing Algorithms for LiDAR Point Cloud Data

LiDAR点云数据实时处理算法

• 批准号: 1228337
• 负责人: Seongjai Kim
• 金额: $ 20.55万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228337&HistoricalAwards=false
• 关键词: Real; time; Processing; Algorithms; LiDAR

The project will develop a real-time processing system for geospatial survey data acquired by light detection and ranging (LiDAR) technology. Surface reconstruction and attribute detection for LiDAR point cloud data are challenging due to natural surface roughness, diverse noises, and huge data sizes. In the existing literature and commercial software, surface reconstruction has been carried out by various methods such as the inverse-distance weighting, kriging, splines, and wavelets. However, the problem is ill-posed and the conventional reconstruction methods either introduce observable interpolation artifacts or become too computationally expensive when the number of data points increases. The investigator will develop and analyze an effective partial differential equation (PDE)-based surface reconstruction algorithm, called the recursive curvature interpolation method (R-CIM), which produces a smooth image surface of a minimum oscillation, and of which the computational cost is in the order of the image size.This project will develop an optimal image reconstruction algorithm for LiDAR point cloud data via collaboration between the Department of Mathematics and Statistics, Mississippi State University, and Agriculture Research Service, United States Department of Agriculture. The proposed algorithm (R-CIM) is optimal in the sense that it possesses a minimum oscillatory behavior and its computational cost is in the order of the image size, independent of the data size. It will contribute to research on image reconstruction and advance various real-time applications towards the real world which involve nonuniformly sampled data. At the same time, the proposed researh includes the development and implementation of the state-of-the-art algorithms for various LiDAR data processing tasks. The project nurtures collaborations between an agricultural engineer and mathematicians having backgrounds on PDEs, numerical analysis, and image processing; it would support a graduate student and an undergraduate student for three years. All the newly-developed software will be freely shared with the community.

该项目将为通过光探测和测距技术获得的地理空间测量数据开发一个实时处理系统。由于自然表面粗糙度、不同的噪音和巨大的数据大小，激光雷达点云数据的表面重建和属性检测具有挑战性。在现有的文献和商业软件中，已经通过各种方法进行了表面重建，例如逆距离加权、克里金、样条和小波。然而，该问题是不适定的，并且当数据点的数量增加时，传统的重建方法要么引入可观察到的插值伪影，要么变得过于计算昂贵。研究人员将开发和分析一种有效的基于偏微分方程（PDE）的表面重建算法，称为递归曲率插值法（R-CIM），它产生一个最小振荡的光滑图像表面，本项目将通过与激光雷达系统的合作，开发一种用于激光雷达点云数据的最佳图像重建算法。密西西比州立大学数学与统计系和美国农业部农业研究处。所提出的算法（R-CIM）是最佳的，在这个意义上，它具有最小的振荡行为，其计算成本是在图像大小的顺序，独立的数据大小。这将有助于图像重建的研究，并推动各种实时应用走向真实的世界，涉及非均匀采样数据。同时，所提出的研究包括开发和实施各种LiDAR数据处理任务的最先进算法。该项目培养了农业工程师和具有偏微分方程，数值分析和图像处理背景的数学家之间的合作;它将支持一名研究生和一名本科生三年。所有新开发的软件都将免费与社区共享。