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.

该项目将开发一种实时处理系统，用于通过光检测和范围（LIDAR）技术获得的地理空间调查数据。由于自然的表面粗糙度，不同的噪声和巨大的数据大小，雷达点云数据的表面重建和属性检测是具有挑战性的。在现有的文献和商业软件中，表面重建是通过各种方法进行的，例如逆距离加权，kriging，spline和小波。但是，这个问题是不适合的，常规的重建方法要么引入可观察到的插值伪像，要么在数据点数量增加时变得过于昂贵。研究者将开发和分析一个有效的部分微分方程（PDE）的表面重建算法，称为递归曲率插入方法（R-CIM），该方法产生了最低振荡的平滑图像表面，并且通过该图的计算成本在图像尺寸的过程中以图像量为单位的计算成本。密西西比州立大学的数学和统计数据，美国农业部的农业研究服务。在具有最小振荡行为的意义上，提出的算法（R-CIM）是最佳的，其计算成本在图像大小的顺序上，与数据大小无关。它将有助于研究图像重建，并将各种实时应用推向现实世界，涉及不均匀采样数据。同时，拟议的研究包括针对各种LIDAR数据处理任务的最先进算法的开发和实施。该项目培养农业工程师和具有PDE，数值分析和图像处理背景的数学家之间的合作；它将支持研究生和本科生三年。所有新开发的软件将与社区免费共享。