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标题：Methods for the identification and removal of outliers from mobile terrestrial LiDAR data
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作者：Michael Leslar ; Jian-Guo Wang ; Baoxin Hu 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 1
出版社：Copernicus Publications
摘要：Terrestrial LiDAR provides many disciplines with an effective and efficient means of producing realistic three-dimensional models of real work objects. With the advent of mobile terrestrial LiDAR, this ability has been expanded to include the rapid collection of three-dimensional models of large urban scenes. For all its usefulness, it does have drawbacks. One of the major problems faced by the LiDAR industry today is the automatic removal of outlying data points from LiDAR point clouds. This paper will discuss the development and implementation of two methods of performing outlier detection in georeferenced point clouds. These methods will make use of the raw data available from most time-of-flight mobile terrestrial LiDAR scanners in both the temporal and spatial domains. The first method involves a moving fixed interval smoother derived from the well-known α -β -γ filter. The second method fits a quadratic curved surface to sections of LiDAR data. The use of these routines is discussed through examples with real LiDAR data
关键词：Outlier Detection; Mobile Terrestrial LiDAR; curved surface fitting; Kalman ; Filter