Map Data Alignment Using Satellite Imagery for GPS Accuracy
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Solution Overview
Problem
Public domain map datasets, such as the TIGER dataset, lack the precision needed for GPS-based navigation due to inaccuracies in latitude/longitude coordinates, leading to potential navigation errors.
Innovation Solution
A method using satellite imagery to identify and update the geographic positions of road segments within map data, ensuring alignment with true coordinates, thereby improving the accuracy of map data for GPS navigation and other applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If public domain map datasets are used, then comprehensive coverage is achieved, but precision and accuracy of geographic coordinates deteriorate
Solution Approach 1:
The system enables map data to self-update by automatically comparing satellite imagery with existing map data, detecting discrepancies, and correcting geographic coordinates without manual intervention. This resolves the contradiction by maintaining high precision through automated processes while preserving productivity.
Solution Approach 2:
The patent replaces manual mechanical updating processes with an automated computational system that uses image processing algorithms to compare satellite imagery with map data and automatically correct coordinates. This substitution achieves both high precision and maintained productivity.
2Reliability
If manual updating of map data is performed, then precision can be improved, but time consumption and labor requirements increase
Solution Approach 1:
The system performs preliminary automated comparison and detection of coordinate discrepancies between satellite imagery and map data before final updates are applied. This preliminary action ensures high reliability by verifying accuracy improvements while reducing the time required for the complete updating process.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously compares satellite imagery with map data, detects inaccuracies, and automatically corrects them. This closed-loop feedback process ensures navigation accuracy is maintained while minimizing time loss through automated iterative improvements.
3Productivity
If satellite imagery processing is automated, then updating speed increases, but system complexity increases
Solution Approach 1:
The patent divides the satellite imagery processing system into distinct functional modules: image acquisition, comparison with map data, discrepancy detection, and automatic correction. This segmentation enables high updating speed through parallel processing while managing complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The system employs a multi-functional processing platform that handles image acquisition, analysis, comparison, and correction operations. This universal system achieves high productivity through integrated operations while reducing overall complexity by consolidating multiple functions into a single coordinated platform.
Data Source
AI summary
Map data are overlaid on satellite imagery. A road segment within the map data is identified, and the satellite imagery indicates that the road segment is at a different geographic position than a geographic position indicated by the map data. The endpoints of the road segment in the map data are aligned with the corresponding positions of the endpoints in the satellite imagery. A road template is applied at an endpoint of the road segment in the satellite imagery, and the angle of the road template that matches the angle of the road segment indicated by the satellite imagery is determined by optimizing a cost function. The road template is iteratively shifted along the road segment in the satellite imagery. The geographic position of the road segment within the map data is updated responsive to the positions and angles of the road template.