Filtering Algorithm for LiDAR Outliers Based on Histogram and KD Tree
LiDARs outliers include points distinctly higher or lower than their surroundings and isolated points, which are normally caused by birds, low flying aircrafts, multi-path errors and system errors. Its necessary to remove LiDARs outliers before classifying LiDAR ground points. In this study, laser points elevations are transformed into a histogram from 0 to 255 elevation scales. Then, the histogram is split by some thresholds with a multilevel segmentation algorithm. A small amount of higher or lower laser points, as they are located at the starting or ending part of the histogram, are filtered into Low Point (noise) class refer to point proportion threshold. In the next step, the algorithm creates an unclassified laser points KD tree and searches the number of points around each laser point in given querying radius. If the number is less than a given point number threshold after increasing search radius length several times, the point is treated as isolated point, i.e. Low Point (noise) class. In experiments, it is shown that this filtering algorithm is reliable in filtering LiDARs outliers.
LiDAR Filtering Outliers Histogram KD tree
Feng Li Zhiwei Yu Bo Wang Qianlin Dong
College of Geosciences and Surveying Engineering China University of Mining and Technology (Beijing), CUMTB Beijing, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
2772-2776
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）