Outlier Detection from LiDAR Data based on the Relative Density

상대적 밀도를 이용한 LiDAR 데이터의 Outlier 검출

LiDAR data often include outliers, the points being signficantly separated from other points and so seeming not to be measured from physical surfaces. Outliers should be removed before processing further the data for applications. Many methods have been developed for other data rather than LiDAR data as a part of data mining processes but their straightforward application to LiDAR data did not provide satisfactory results. In this study, we have thus modified one of such methods by considering the properties of LiDAR data and developed a method based on the relative point density. The proposed method have been applied to simulated and real data. The results confirms its promising performance with respect to the processing time and the detection accuracy