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| Experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wings UAVs |
| A.Benini,M.J.Rutherford,K.P.Valavanis |
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| (DU Unmanned Systems Research Institute (DU2SRI), University of Denver (DU), Denver, CO 80208, U.S.A.) |
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| 摘要: |
| This paper proposes a real-time system for pose estimation of an unmanned aerial vehicle (UAV) using parallel image processing and a fiducial marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests using a custom quadrotor. The autonomous landing is successfully demonstrated, through experimental tests, using the proposed algorithm. The results show that the system is able to provide precise pose estimation with a framerate of at least 30\,fps and an image resolution of 640?480 pixels. The main advantage of the proposed approach is in the use of the GPU for image filtering and marker detection. The GPU provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments. |
| 关键词: UAV, Vision, GPU, Kalman filter |
| DOI： |
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| 基金项目:This work was partially supported by the National Science Foundation (NSF) under the Computer and Network Systems (CNS) award (Nos. 1229236, 1446285). |
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| Experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wings UAVs |
| A. Benini,M. J. Rutherford,K. P. Valavanis |
| (DU Unmanned Systems Research Institute (DU2SRI), University of Denver (DU), Denver, CO 80208, U.S.A.) |
| Abstract: |
| This paper proposes a real-time system for pose estimation of an unmanned aerial vehicle (UAV) using parallel image processing and a fiducial marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests using a custom quadrotor. The autonomous landing is successfully demonstrated, through experimental tests, using the proposed algorithm. The results show that the system is able to provide precise pose estimation with a framerate of at least 30\,fps and an image resolution of 640?480 pixels. The main advantage of the proposed approach is in the use of the GPU for image filtering and marker detection. The GPU provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments. |
| Key words: UAV, Vision, GPU, Kalman filter |
