Distributed estimation of unmanned surface vessel based on Kalman-consensus filter and convolutional neural network |
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| Institution: | 1. Key Laboratory of Imaging Processing and Intelligence Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China;1. Faculty of Applied Science, University of British Columbia, Vancouver, Canada;2. College of Nursing, University of Saskatchewan, Regina, Canada;3. Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK;4. Department of Oncology, KU Leuven - University of Leuven, Leuven, Belgium;5. University Hospital Waterford, Waterford, Ireland;6. Faculty of Medicine, University of Chile, Santiago, Chile;7. University Hospitals Sussex NHS Foundation Trust, Pharmacy, Brighton, UK;8. School of Nursing, University of Victoria, Victoria, Canada;9. School of Healthcare, University of Leicester, Leicester, UK;10. Princess Margaret Cancer Centre, Toronto, Canada;11. University of Groningen, University Medical Center Groningen, Groningen, the Netherlands;12. Faculty of Nursing, University of Calgary, Calgary, Canada;13. Select Medical, ReVital Cancer Rehabilitation, Mechanicsburg PA, USA;14. University of Saskatchewan, Saskatoon, Canada;15. Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Canada;p. Department of General Medical Oncology and Geriatric Medicine, University Hospitals Leuven, Leuven, Belgium;q. Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven - University of Leuven, Leuven, Belgium;1. Kaiser Permanente Northwest, Center for Health Research, United States of America;2. University of California, Los Angeles, Departments of Biostatistics, Statistics, and Psychiatry, United States of America;3. University of California, Los Angeles, Department of Psychiatry and Biobehavioral Sciences, United States of America |
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| Abstract: | The distributed estimation has important research significance in unmanned systems. This paper investigates the distributed estimation of unmanned surface vessel (USV) via multi-sensor collaboration and 3D object recognition, in which a Knowledge Graph (KG) is constructed to store and represent the estimation results. Kalman-consensus Filter (KCF) and convolutional neural network (CNN) are used to estimate the optimal states of objects, and recognise multiple classes of objects without designing detectors for each class of objects, respectively. The recognition efficiency is improved by dividing the data into pixel blocks whose value is the number of detection points, and a point cloud dataset in different locations and rotations is also provided. Experiments are proposed to show that our method can help the USV accurately perceive entities in the environment, which validates the effectiveness of the proposed algorithm. |
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