Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera
Robot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSen...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9152035 |
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| _version_ | 1832547733687238656 |
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| author | Li-fen Tu Qi Peng |
| author_facet | Li-fen Tu Qi Peng |
| author_sort | Li-fen Tu |
| collection | DOAJ |
| description | Robot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSense camera data. By using this method, any current single-camera detection system can be upgraded online. Without changing the original system, the depth information of the original monocular camera can be obtained simply, and the transition from 2D detection to 3D detection can be realized. In order to verify the effectiveness of the proposed method, a hardware system was constructed using the Micro-vision RS-A14K-GC8 industrial camera and the Intel RealSense D415 depth camera, and the depth map fitting algorithm proposed in this paper was used to test the system. The results show that, except for a few depth-missing areas, the results of other areas with depth are still good, which can basically describe the distance difference between the target and the camera. In addition, in order to verify the scalability of the method, a new hardware system was constructed with different cameras, and images were collected in a complex farmland environment. The generated depth map was good, which could basically describe the distance difference between the target and the camera. |
| format | Article |
| id | doaj-art-c31fc907f1014789ae48e4e5091de174 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-c31fc907f1014789ae48e4e5091de1742025-02-03T06:43:33ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/91520359152035Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular CameraLi-fen Tu0Qi Peng1School of Physics and Electronic Information Engineering, Hubei Engineering University, Xiaogan 432000, ChinaSchool of Physics and Electronic Information Engineering, Hubei Engineering University, Xiaogan 432000, ChinaRobot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSense camera data. By using this method, any current single-camera detection system can be upgraded online. Without changing the original system, the depth information of the original monocular camera can be obtained simply, and the transition from 2D detection to 3D detection can be realized. In order to verify the effectiveness of the proposed method, a hardware system was constructed using the Micro-vision RS-A14K-GC8 industrial camera and the Intel RealSense D415 depth camera, and the depth map fitting algorithm proposed in this paper was used to test the system. The results show that, except for a few depth-missing areas, the results of other areas with depth are still good, which can basically describe the distance difference between the target and the camera. In addition, in order to verify the scalability of the method, a new hardware system was constructed with different cameras, and images were collected in a complex farmland environment. The generated depth map was good, which could basically describe the distance difference between the target and the camera.http://dx.doi.org/10.1155/2021/9152035 |
| spellingShingle | Li-fen Tu Qi Peng Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera Journal of Electrical and Computer Engineering |
| title | Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera |
| title_full | Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera |
| title_fullStr | Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera |
| title_full_unstemmed | Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera |
| title_short | Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera |
| title_sort | method of using realsense camera to estimate the depth map of any monocular camera |
| url | http://dx.doi.org/10.1155/2021/9152035 |
| work_keys_str_mv | AT lifentu methodofusingrealsensecameratoestimatethedepthmapofanymonocularcamera AT qipeng methodofusingrealsensecameratoestimatethedepthmapofanymonocularcamera |