A New Approach toward Corner Detection for Use in Point Cloud Registration
For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/13/3375 |
| _version_ | 1797590902143713280 |
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| author | Wei Wang Yi Zhang Gengyu Ge Huan Yang Yue Wang |
| author_facet | Wei Wang Yi Zhang Gengyu Ge Huan Yang Yue Wang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>4</mn></msup></semantics></math></inline-formula> points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>6</mn></msup></semantics></math></inline-formula> points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications. |
| first_indexed | 2024-03-11T01:30:09Z |
| format | Article |
| id | doaj.art-36eb51d8dd8749ac991d02f75265a867 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:30:09Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-36eb51d8dd8749ac991d02f75265a8672023-11-18T17:25:21ZengMDPI AGRemote Sensing2072-42922023-07-011513337510.3390/rs15133375A New Approach toward Corner Detection for Use in Point Cloud RegistrationWei Wang0Yi Zhang1Gengyu Ge2Huan Yang3Yue Wang4College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongwen Road, Nan’an, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongwen Road, Nan’an, Chongqing 400065, ChinaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongwen Road, Nan’an, Chongqing 400065, ChinaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongwen Road, Nan’an, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongwen Road, Nan’an, Chongqing 400065, ChinaFor this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>4</mn></msup></semantics></math></inline-formula> points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>6</mn></msup></semantics></math></inline-formula> points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications.https://www.mdpi.com/2072-4292/15/13/3375plane/corner detectionpoint cloud registrationcoarse to finegraph matchingpoint cloud processingRGB-D Camera |
| spellingShingle | Wei Wang Yi Zhang Gengyu Ge Huan Yang Yue Wang A New Approach toward Corner Detection for Use in Point Cloud Registration Remote Sensing plane/corner detection point cloud registration coarse to fine graph matching point cloud processing RGB-D Camera |
| title | A New Approach toward Corner Detection for Use in Point Cloud Registration |
| title_full | A New Approach toward Corner Detection for Use in Point Cloud Registration |
| title_fullStr | A New Approach toward Corner Detection for Use in Point Cloud Registration |
| title_full_unstemmed | A New Approach toward Corner Detection for Use in Point Cloud Registration |
| title_short | A New Approach toward Corner Detection for Use in Point Cloud Registration |
| title_sort | new approach toward corner detection for use in point cloud registration |
| topic | plane/corner detection point cloud registration coarse to fine graph matching point cloud processing RGB-D Camera |
| url | https://www.mdpi.com/2072-4292/15/13/3375 |
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