SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION
This paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method -- threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the r...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B5/493/2016/isprs-archives-XLI-B5-493-2016.pdf |
| _version_ | 1811198349111459840 |
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| author | Z. Kang R. Lindenbergh S. Pu |
| author_facet | Z. Kang R. Lindenbergh S. Pu |
| author_sort | Z. Kang |
| collection | DOAJ |
| description | This paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method -- threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the random measurement error and then approach the real registration error. BaySAC and other basic sampling algorithms usually need to artificially determine a threshold by which inlier points are identified, which leads to a threshold-dependent verification process. Therefore, we applied the LMedS method to construct the cost function that is used to determine the optimum model to reduce the influence of human factors and improve the robustness of the model estimate. Point-to-point and point-to-surface error metrics are most commonly used. However, point-to-point error in general consists of at least two components, random measurement error and systematic error as a result of a remaining error in the found rigid body transformation. Thus we employ the measure of the average point-to-surface residual to evaluate the registration accuracy. The proposed approaches, together with a traditional RANSAC approach, are tested on four data sets acquired by three different scanners in terms of their computational efficiency and quality of the final registration. The registration results show the st.dev of the average point-to-surface residuals is reduced from 1.4 cm (plain RANSAC) to 0.5 cm (threshold-independent BaySAC). The results also show that, compared to the performance of RANSAC, our BaySAC strategies lead to less iterations and cheaper computational cost when the hypothesis set is contaminated with more outliers. |
| first_indexed | 2024-04-12T01:30:03Z |
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| id | doaj.art-b9006537bdfb4500a0821bc9df3293f3 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T01:30:03Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-b9006537bdfb4500a0821bc9df3293f32022-12-22T03:53:32ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B549350010.5194/isprs-archives-XLI-B5-493-2016SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTIONZ. Kang0R. Lindenbergh1S. Pu2Department of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, ChinaChair Optical and Laser Remote Sensing, Dept. Geosciences and Remote Sensing, Fact. Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The NetherlandsBeijing Tovos Technology Co., Ltd, Unit 1602, TOP Building Tower B, Haidian Dajie No.3, Haidian District. Beijing, ChinaThis paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method -- threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the random measurement error and then approach the real registration error. BaySAC and other basic sampling algorithms usually need to artificially determine a threshold by which inlier points are identified, which leads to a threshold-dependent verification process. Therefore, we applied the LMedS method to construct the cost function that is used to determine the optimum model to reduce the influence of human factors and improve the robustness of the model estimate. Point-to-point and point-to-surface error metrics are most commonly used. However, point-to-point error in general consists of at least two components, random measurement error and systematic error as a result of a remaining error in the found rigid body transformation. Thus we employ the measure of the average point-to-surface residual to evaluate the registration accuracy. The proposed approaches, together with a traditional RANSAC approach, are tested on four data sets acquired by three different scanners in terms of their computational efficiency and quality of the final registration. The registration results show the st.dev of the average point-to-surface residuals is reduced from 1.4 cm (plain RANSAC) to 0.5 cm (threshold-independent BaySAC). The results also show that, compared to the performance of RANSAC, our BaySAC strategies lead to less iterations and cheaper computational cost when the hypothesis set is contaminated with more outliers.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B5/493/2016/isprs-archives-XLI-B5-493-2016.pdf |
| spellingShingle | Z. Kang R. Lindenbergh S. Pu SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION |
| title_full | SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION |
| title_fullStr | SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION |
| title_full_unstemmed | SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION |
| title_short | SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION |
| title_sort | speeding up coarse point cloud registration by threshold independent baysac match selection |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B5/493/2016/isprs-archives-XLI-B5-493-2016.pdf |
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