ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA
This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete da...
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Copernicus Publications
2018
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oai:localhost:DHQB_123456789-40792018-10-22T08:45:04Z ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences A. Nurunnabi (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) Y. Sadahiro (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) R. Lindenbergh (Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands) Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling. 12,106 Journals 9,040 searchable at Article level 128 Countries 3,341,785 Articles FAQs OAI-PMH, XML, Widgets Open Access Resources Best Practice Download metadata New Journals Feed Facilitating funding for sustainable OA (includes SCOSS) Our members Our publisher members Our sponsors Our volunteers 2018-09-12T02:26:10Z 2018-09-12T02:26:10Z 2017 Other http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4079 Copernicus Publications |
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Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
| collection |
Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
| topic |
Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics |
| spellingShingle |
Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics A. Nurunnabi (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) Y. Sadahiro (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) R. Lindenbergh (Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands) ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| description |
This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.
12,106 Journals
9,040 searchable at Article level
128 Countries
3,341,785 Articles
FAQs
OAI-PMH, XML, Widgets
Open Access Resources
Best Practice
Download metadata
New Journals Feed
Facilitating funding for sustainable OA (includes SCOSS)
Our members
Our publisher members
Our sponsors
Our volunteers |
| format |
Other |
| author |
A. Nurunnabi (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) Y. Sadahiro (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) R. Lindenbergh (Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands) |
| author_facet |
A. Nurunnabi (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) Y. Sadahiro (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) R. Lindenbergh (Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands) |
| author_sort |
A. Nurunnabi (Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan) |
| title |
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| title_short |
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| title_full |
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| title_fullStr |
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| title_full_unstemmed |
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA |
| title_sort |
robust cylinder fitting in three-dimensional point cloud data |
| publisher |
Copernicus Publications |
| publishDate |
2018 |
| url |
http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4079 |
| _version_ |
1717292472315412480 |
| score |
9,463379 |