Watercraft Radar SLAM Docking With GNSS-Limited Map Correction
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Solution Overview
Problem
Existing GNSS-based map data for watercraft navigation is often inaccurate and imprecise, leading to potential collisions and safety risks due to angular and positional offsets, especially in areas with limited satellite coverage or obstructions, hindering autonomous navigation and docking operations.
Innovation Solution
Utilizing radar and Simultaneous Localization And Mapping (SLAM) techniques to generate updated map data, filtering out transient objects, and providing navigation instructions for precise docking, even in areas with limited GNSS coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS-based map data is used for watercraft navigation, then navigation functionality is provided, but accuracy and precision deteriorate with five to ten feet positional offset
Solution Approach 1:
The patent combines GNSS data with radar data and SLAM processing to create a hybrid positioning system. The radar provides high-precision relative position data while SLAM algorithms fuse multiple data sources including inertial measurements, creating a more reliable positioning system that maintains accuracy when GNSS signals are unavailable or inaccurate.
Solution Approach 2:
The patent introduces SLAM processing as an intermediary layer between raw sensor data and navigation output. This intermediary system processes radar returns, inertial data, and GNSS signals through algorithmic fusion to produce corrected map data and position estimates, mediating the inaccuracies of individual sensors.
2Measurement precision
If traditional radar processing techniques are used, then simple processing is maintained, but map data precision deteriorates with five to ten feet offset from actual location
Solution Approach 1:
SLAM processing serves as an intermediary computational layer that transforms traditional radar data into high-precision position information. The algorithm mediates between simple radar measurements and complex navigation requirements, providing accurate object positioning without requiring complex hardware modifications.
Solution Approach 2:
The patent replaces mechanical/GNSS-based positioning with algorithmic SLAM processing. Instead of relying on satellite-based mechanical positioning systems, the system uses computational methods to process radar returns and inertial data, substituting algorithmic complexity for hardware complexity while achieving superior precision.
3Adaptability or versatility
If map data is used in areas with satellite obstructions, then navigation coverage is extended, but data availability deteriorates due to limited satellite coverage
Solution Approach 1:
The patent extracts positioning capability from GNSS dependency by implementing SLAM processing that uses radar and inertial sensors as primary data sources. This extraction removes the system's dependence on satellite availability, enabling navigation in areas with limited or obstructed GNSS coverage while maintaining positioning functionality.
Solution Approach 2:
The system changes the operational parameters of the navigation system by switching from GNSS-primary to SLAM-primary mode when satellite coverage is limited. This parameter change allows the system to adapt to different environmental conditions, maintaining navigation coverage in areas where traditional GNSS-based systems would fail.
4Measurement precision
If watercraft components are misaligned, then device assembly is simplified, but map data accuracy deteriorates with angular offset from correct orientation
Solution Approach 1:
The SLAM processing system provides continuous feedback on the watercraft's orientation and position by comparing expected radar returns with actual measurements. This feedback mechanism detects and compensates for angular misalignments of components, maintaining map data accuracy without requiring precise mechanical alignment during assembly.
Solution Approach 2:
The system performs self-correction of alignment errors through SLAM algorithms that automatically detect and compensate for angular offsets in map data. Rather than requiring external calibration or precise assembly, the system serves itself by algorithmically correcting for component misalignment, reducing assembly complexity requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy and precision of map data, enabling safer and more precise watercraft navigation and docking operations, including autonomous or semi-autonomous guidance, by refining map data over time and filtering transient objects.
Implementation Method 1
receiving first radar data from a radar on the watercraft. The first radar data is associated with a first coverage area
Data Source
AI summary
A method for forming updated map data and for using the updated map data to assist in docking the watercraft is provided. The method includes receiving first radar data from radar. First radar data is associated with a first coverage area. The method includes generating initial map data regarding features of the environment around the watercraft based on the first radar data. The method includes receiving second radar data associated with a location within the first coverage area from the radar. The first and second radar data are different. The method includes updating initial map data based on the second radar data to form updated map data and generating a docking operation using updated map data. The docking operation comprises causing presentation of a docking view illustrating a watercraft representation and a desired docking position and/or generating navigation instruction(s) for causing the watercraft to be repositioned.