Title: A Sea-Sky Line Detection Aided GNSS/INS Integration Method for Unmanned Surface Vehicle Navigation
Author(s): Li Fu, Changqing Hu and Lingbing Kong
Published in: Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
Portland, Oregon
Pages: 1809 - 1815
Cite this article: Fu, Li, Hu, Changqing, Kong, Lingbing, "A Sea-Sky Line Detection Aided GNSS/INS Integration Method for Unmanned Surface Vehicle Navigation," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 1809-1815.
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Abstract: With the fast development of unmanned systems technology and artificial intelligence, Unmanned Surface Vehicle (USV) following unmanned aerial vehicle and unmanned vehicle is becoming more and more popular in military and civilian applications, such as hydrographic survey, maritime search and rescue, coastal water environment monitoring, 3D mapping and enemy reconnaissance. One of the core technologies of the USV is the precise and autonomous navigation system, which is essential for the USV accomplishing a variety of missions without human interventions. Usually, Global Navigation Satellite Systems (GNSS) is integrated with Inertial Navigation System (INS) or other embedded sensors to obtain more accurate position, attitude, speed, heading, etc. To meet the high performance requirements for USV navigation, a sea-sky line detection aided GNSS/INS integration method is proposed in this paper. The proposed method uses sea-sky line as an aid to GNSS/INS integration for USV navigation, which uses the different system to compensate the drawbacks of each source. First, a novel image-based sea-sky line detection method is presented. Then, the detected sea-sky line is applied into a geometric model to determine the roll and pitch angle of the USV. Finally, the difference equation of the SSL is integrated into the GNSS/INS hybridization architecture to improve the performance of the navigation system. Experimental results demonstrate the effectiveness of our proposed algorithm over the existing GNSS/INS integration method in the navigation parameters estimation, which can be applied for USVs with a camera system available on board.