Robust Locally Weighted Regression For Ground Surface Extraction In Mobile Laser Scanning 3D Data
Clicks: 364
ID: 53140
2013
A new robust way for ground surface extraction from mobile laser scanning 3D point cloud data is proposed in this paper. Fitting
polynomials along 2D/3D points is one of the well-known methods for filtering ground points, but it is evident that unorganized
point clouds consist of multiple complex structures by nature so it is not suitable for fitting a parametric global model. The aim of
this research is to develop and implement an algorithm to classify ground and non-ground points based on statistically robust locally
weighted regression which fits a regression surface (line in 2D) by fitting without any predefined global functional relation among
the variables of interest. Afterwards, the z (elevation)-values are robustly down weighted based on the residuals for the fitted points.
The new set of down weighted z-values along with x (or y) values are used to get a new fit of the (lower) surface (line). The process
of fitting and down-weighting continues until the difference between two consecutive fits is insignificant. Then the final fit
represents the ground level of the given point cloud and the ground surface points can be extracted. The performance of the new
method has been demonstrated through vehicle based mobile laser scanning 3D point cloud data from urban areas which include
different problematic objects such as short walls, large buildings, electric poles, sign posts and cars. The method has potential in
areas like building/construction footprint determination, 3D city modelling, corridor mapping and asset management.
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nurunnabi2013robustisprs
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| Authors | Nurunnabi, A.;West, G.;Belton, D.; |
| Journal | isprs annals of the photogrammetry, remote sensing and spatial information sciences |
| Year | 2013 |
| DOI | DOI not found |
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