Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)
The Unmanned Aerial Vehicle (UAV) is one of the most remarkable inventions of the last 100 years. Much research has been invested in the development of this flying robot. The landing system is one of the more challenging aspects of this system’s development. Artificial Intelligence (AI) has become t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/5/1870 |
| _version_ | 1797473782014672896 |
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| author | Mohammad Sefidgar Rene Landry |
| author_facet | Mohammad Sefidgar Rene Landry |
| author_sort | Mohammad Sefidgar |
| collection | DOAJ |
| description | The Unmanned Aerial Vehicle (UAV) is one of the most remarkable inventions of the last 100 years. Much research has been invested in the development of this flying robot. The landing system is one of the more challenging aspects of this system’s development. Artificial Intelligence (AI) has become the preferred technique for landing system development, including reinforcement learning. However, current research is more focused is on system development based on image processing and advanced geometry. A novel calibration based on our previous research had been used to ameliorate the accuracy of the AprilTag pose estimation. With the help of advanced geometry from camera and range sensor data, a process known as Inverse Homography Range Camera Fusion (IHRCF), a pose estimation that outperforms our previous work, is now possible. The range sensor used here is a Time of Flight (ToF) sensor, but the algorithm can be used with any range sensor. First, images are captured by the image acquisition device, a monocular camera. Next, the corners of the landing landmark are detected through AprilTag detection algorithms (ATDA). The pixel correspondence between the image and the range sensor is then calculated via the calibration data. In the succeeding phase, the planar homography between the real-world locations of sensor data and their obtained pixel coordinates is calculated. In the next phase, the pixel coordinates of the AprilTag-detected four corners are transformed by inverse planar homography from pixel coordinates to world coordinates in the camera frame. Finally, knowing the world frame corner points of the AprilTag, rigid body transformation can be used to create the pose data. A CoppeliaSim simulation environment was used to evaluate the IHRCF algorithm, and the test was implemented in real-time Software-in-the-Loop (SIL). The IHRCF algorithm outperformed the AprilTag-only detection approach significantly in both translational and rotational terms. To conclude, the conventional landmark detection algorithm can be ameliorated by incorporating sensor fusion for cameras with lower radial distortion. |
| first_indexed | 2024-03-09T20:21:19Z |
| format | Article |
| id | doaj.art-5b715e3b9db5481985c4ff1584638831 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:19Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5b715e3b9db5481985c4ff15846388312023-11-23T23:47:32ZengMDPI AGSensors1424-82202022-02-01225187010.3390/s22051870Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)Mohammad Sefidgar0Rene Landry1LASSENA Laboratory, École de Technologies Supérieure (ÉTS), Montreal, QC H3C 1K3, CanadaLASSENA Laboratory, École de Technologies Supérieure (ÉTS), Montreal, QC H3C 1K3, CanadaThe Unmanned Aerial Vehicle (UAV) is one of the most remarkable inventions of the last 100 years. Much research has been invested in the development of this flying robot. The landing system is one of the more challenging aspects of this system’s development. Artificial Intelligence (AI) has become the preferred technique for landing system development, including reinforcement learning. However, current research is more focused is on system development based on image processing and advanced geometry. A novel calibration based on our previous research had been used to ameliorate the accuracy of the AprilTag pose estimation. With the help of advanced geometry from camera and range sensor data, a process known as Inverse Homography Range Camera Fusion (IHRCF), a pose estimation that outperforms our previous work, is now possible. The range sensor used here is a Time of Flight (ToF) sensor, but the algorithm can be used with any range sensor. First, images are captured by the image acquisition device, a monocular camera. Next, the corners of the landing landmark are detected through AprilTag detection algorithms (ATDA). The pixel correspondence between the image and the range sensor is then calculated via the calibration data. In the succeeding phase, the planar homography between the real-world locations of sensor data and their obtained pixel coordinates is calculated. In the next phase, the pixel coordinates of the AprilTag-detected four corners are transformed by inverse planar homography from pixel coordinates to world coordinates in the camera frame. Finally, knowing the world frame corner points of the AprilTag, rigid body transformation can be used to create the pose data. A CoppeliaSim simulation environment was used to evaluate the IHRCF algorithm, and the test was implemented in real-time Software-in-the-Loop (SIL). The IHRCF algorithm outperformed the AprilTag-only detection approach significantly in both translational and rotational terms. To conclude, the conventional landmark detection algorithm can be ameliorated by incorporating sensor fusion for cameras with lower radial distortion.https://www.mdpi.com/1424-8220/22/5/1870inverse planar homographysensor fusionnavigation landing system designpose estimation |
| spellingShingle | Mohammad Sefidgar Rene Landry Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) Sensors inverse planar homography sensor fusion navigation landing system design pose estimation |
| title | Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) |
| title_full | Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) |
| title_fullStr | Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) |
| title_full_unstemmed | Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) |
| title_short | Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF) |
| title_sort | landing system development based on inverse homography range camera fusion ihrcf |
| topic | inverse planar homography sensor fusion navigation landing system design pose estimation |
| url | https://www.mdpi.com/1424-8220/22/5/1870 |
| work_keys_str_mv | AT mohammadsefidgar landingsystemdevelopmentbasedoninversehomographyrangecamerafusionihrcf AT renelandry landingsystemdevelopmentbasedoninversehomographyrangecamerafusionihrcf |