Unmanned ship near-shore navigation mapping stable observation identification and shoreline constraint method
By constructing the unmanned vessel state vector and observation disturbance index, calculating the stable observation confidence, adjusting the point cloud matching information, and using shoreline soft constraints and global navigation satellite system residuals for joint optimization, the problem of unstable navigation and mapping of unmanned vessels in nearshore waters is solved, the stability of observation screening and shoreline structure continuity are improved, and a reliable navigation and mapping foundation is provided.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG UNIV OF SCI & TECH
- Filing Date
- 2026-06-25
- Publication Date
- 2026-07-24
AI Technical Summary
The navigation and mapping of unmanned surface vessels in nearshore waters suffers from instability and low accuracy due to dynamic disturbances on the water surface, attitude disturbances, and lack of shoreline constraints.
By constructing the state vector of the unmanned vessel, the neighborhood geometric features and semantic labels of the laser points are obtained. The nearshore observation disturbance index and semantic and geometric consistency scores are calculated. Map units are divided and temporal stability scores are calculated. Stable observation confidence is constructed. The point cloud matching information matrix is adjusted. Joint optimization is performed using shoreline soft constraints and global navigation satellite system residuals to trigger a conservative mapping strategy and form a stable observation confidence layer and a shoreline structure layer, providing reliable basic data for subsequent navigation mapping.
It improves the stability of observation and screening of unmanned surface vessels in complex nearshore waters, reduces the impact of water surface reflection, wave trails and foam echoes on static structure maps, enhances the continuity of shoreline structure and the stability of unmanned surface vessel pose estimation, and provides a reliable basis for navigation and mapping.
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