A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs
This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equ...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/35 |
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| author | Jie Yang Kunling He Jie Zhang Jiacheng Li Qian Chen Xiaohui Wei Hanlin Sheng |
| author_facet | Jie Yang Kunling He Jie Zhang Jiacheng Li Qian Chen Xiaohui Wei Hanlin Sheng |
| author_sort | Jie Yang |
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| description | This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with <i>x</i> and <i>y</i>-axis errors not exceeding 0.01 m in close range, the <i>x</i>-axis relative error not exceeding 0.05 m, and the <i>y</i>-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system. |
| format | Article |
| id | doaj-art-ca81f9f432004ee2a8fd767e4b895ff6 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-ca81f9f432004ee2a8fd767e4b895ff62025-01-24T13:29:43ZengMDPI AGDrones2504-446X2025-01-01913510.3390/drones9010035A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVsJie Yang0Kunling He1Jie Zhang2Jiacheng Li3Qian Chen4Xiaohui Wei5Hanlin Sheng6College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, ChinaThis paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with <i>x</i> and <i>y</i>-axis errors not exceeding 0.01 m in close range, the <i>x</i>-axis relative error not exceeding 0.05 m, and the <i>y</i>-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system.https://www.mdpi.com/2504-446X/9/1/35visual servoingtarget recognitionautonomous landingApriltag fiducial systemmodel predictive control (MPC) |
| spellingShingle | Jie Yang Kunling He Jie Zhang Jiacheng Li Qian Chen Xiaohui Wei Hanlin Sheng A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs Drones visual servoing target recognition autonomous landing Apriltag fiducial system model predictive control (MPC) |
| title | A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs |
| title_full | A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs |
| title_fullStr | A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs |
| title_full_unstemmed | A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs |
| title_short | A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs |
| title_sort | binocular vision assisted method for the accurate positioning and landing of quadrotor uavs |
| topic | visual servoing target recognition autonomous landing Apriltag fiducial system model predictive control (MPC) |
| url | https://www.mdpi.com/2504-446X/9/1/35 |
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