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Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data
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| Zeitschriftentitel: | Scientific Programming |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Scientific Programming, 2017, 2017, S. 1-10 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Hindawi Limited
|
| Schlagwörter: |
| author_facet |
Dong, Mingjie Chou, Wusheng Fang, Bin Dong, Mingjie Chou, Wusheng Fang, Bin |
|---|---|
| author |
Dong, Mingjie Chou, Wusheng Fang, Bin |
| spellingShingle |
Dong, Mingjie Chou, Wusheng Fang, Bin Scientific Programming Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data Computer Science Applications Software |
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dong, mingjie |
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Dong, Mingjie Chou, Wusheng Fang, Bin 1058-9244 1875-919X Hindawi Limited Computer Science Applications Software http://dx.doi.org/10.1155/2017/7136702 <jats:p>In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.</jats:p> Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data Scientific Programming |
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| title |
Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
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Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_full |
Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
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Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
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Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_short |
Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
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underwater matching correction navigation based on geometric features using sonar point cloud data |
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Computer Science Applications Software |
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http://dx.doi.org/10.1155/2017/7136702 |
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2017 |
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1-10 |
| description |
<jats:p>In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.</jats:p> |
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| author | Dong, Mingjie, Chou, Wusheng, Fang, Bin |
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| description | <jats:p>In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.</jats:p> |
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| spelling | Dong, Mingjie Chou, Wusheng Fang, Bin 1058-9244 1875-919X Hindawi Limited Computer Science Applications Software http://dx.doi.org/10.1155/2017/7136702 <jats:p>In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.</jats:p> Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data Scientific Programming |
| spellingShingle | Dong, Mingjie, Chou, Wusheng, Fang, Bin, Scientific Programming, Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data, Computer Science Applications, Software |
| title | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_full | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_fullStr | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_full_unstemmed | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_short | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| title_sort | underwater matching correction navigation based on geometric features using sonar point cloud data |
| title_unstemmed | Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data |
| topic | Computer Science Applications, Software |
| url | http://dx.doi.org/10.1155/2017/7136702 |