A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing
نویسندگان
چکیده
Autonomous underwater vehicles (AUV) recycling in an environment is particularly challenging due to the continuous exploitation of marine resources. AUV via visual technology primary method. However, current limited by harsh sea conditions and has problems, such as poor tracking detection. To solve these we propose a long-term target anti-interference (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks online learning ideas. Meanwhile, using cubature Kalman filter (CKF) for optimization prediction position. We constructed launch recovery system (LARS) capturing AUV. The consists following parts: First, images are acquired binocular cameras. Next, relative position between end LARS was estimated based on pixel positions feature points camera data. Finally, discrete proportion integration differentiation (PID) controlled capture moving CKF-optimized verify feasibility our proposed system, used robot operating (ROS) platform Gazebo software simulate experiments visualization. experiment demonstrates that process when makes sinusoidal motion with amplitude 0.2 m three-dimensional space distance no more than 1 m, error does not exceed 0.03 m. In process, final about 28 mm. Our results high robustness accuracy, providing foundation future research.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15030748