USV Positioning Using Laser Radar and Sonar Fusion
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Solution Overview
Problem
Unmanned surface vehicles face imprecision in surveying and mapping due to unstable inertial navigation system signals, especially when passing bridge openings or areas with shelters, and challenges in centimeter-level positioning and three-dimensional point cloud mapping using multiple sensors.
Innovation Solution
A nearshore real-time positioning and mapping method utilizing multiple distance measuring sensors, including inertia measurement units, laser radars, and sonar, which acquires predicted gesture data, processes radar point cloud data, and constructs a three-dimensional cloud point pattern through interframe constraint optimization and factor graph techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If inertial navigation system is used for positioning, then the unmanned surface vehicle can navigate autonomously, but positioning precision deteriorates when passing bridge openings or areas with shelters due to unstable signals
Solution Approach 1:
The patent combines multiple positioning systems (inertial navigation, laser radar, sonar, and external references like bridge openings or shutters) into a unified positioning framework. By merging these different sensing modalities, the system maintains autonomous navigation capability while compensating for the instability of individual systems in challenging environments.
Solution Approach 2:
The patent introduces intermediary reference objects (such as bridge openings, shutters, or other fixed structures in the environment) that serve as mediators between the unmanned surface vehicle and the positioning system. These intermediaries provide stable geometric references that help correct positioning errors when traditional inertial navigation signals become unstable.
2Measurement precision
If multiple laser radar and sonar sensors are used for mapping, then three-dimensional point cloud map can be constructed, but device complexity increases
Solution Approach 1:
The patent designs a multi-sensor system where laser radar and sonar serve multiple functions: laser radar provides both positioning information and surface mapping data, while sonar provides both positioning information and underwater/under-structure mapping data. This multi-functionality reduces the need for separate dedicated sensors for each task, thereby managing complexity while maintaining high mapping precision.
Solution Approach 2:
The patent creates a composite sensing system that integrates different types of sensors (laser radar for optical detection, sonar for acoustic detection) into a unified mapping framework. This composite approach leverages the complementary strengths of each sensor type to achieve high-precision three-dimensional mapping while managing system complexity through integrated processing.
3Ease of operation
If inertial navigation system is used, then gesture estimation can be performed, but error accumulation occurs leading to imprecise surveying and mapping
Solution Approach 1:
The patent implements a feedback mechanism where the unmanned surface vehicle continuously compares its estimated position and orientation (from inertial navigation) with observations from laser radar and sonar measurements of known environmental features. This feedback loop allows the system to detect and correct error accumulation in real-time, maintaining high surveying and mapping precision despite the inherent drift of inertial navigation systems.
Solution Approach 2:
The patent performs preliminary calibration and registration of the multi-sensor system before operation, establishing accurate spatial relationships between sensors and the vehicle body. This preliminary action ensures that gesture estimation from inertial navigation is properly aligned with the coordinate systems of other sensors, reducing initial error sources and improving overall measurement precision.
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
This method achieves high precision local positioning and centimeter-level global positioning, enabling accurate three-dimensional point cloud mapping and robust navigation in nearshore environments, even in complex conditions.
Implementation Method 1
acquiring radar point cloud data by a laser radar
Implementation Method 2
each sonar point cloud data
Implementation Method 3
a Doppler velocimeter factor
Data Source
AI summary
A nearshore real-time positioning and mapping method for an unmanned surface vehicle with multiple distance measuring sensors comprises: acquiring predicted gesture data of the unmanned surface vehicle by an inertia measurement unit; acquiring radar point cloud data by a laser radar, projecting the radar point cloud data to a depth map, and reserving ground points and break points on the depth map; dividing the depth map into six sub depth maps, obtaining a feature point set via a curvature of each laser point, and converting all the feature point sets of the laser radar into coordinates of the unmanned surface vehicle; obtaining a relative gesture transformation matrix of the current unmanned surface vehicle via the radar cloud point data of two adjacent frames; accruing multiple factors, and optimizing a gesture of the unmanned surface vehicle in form of a factor pattern; and constructing a three-dimensional cloud point pattern.