Map Data Fusion Elevation Conversion
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Solution Overview
Problem
Existing methods for fusing two-dimensional and three-dimensional map data face challenges in accurately aligning three-dimensional map data with two-dimensional map data, especially due to varying elevation values, leading to issues like floating objects in the air and high labor costs for manual corrections.
Innovation Solution
A method that involves obtaining and classifying two-dimensional and three-dimensional map data, performing elevation conversion using preset algorithms specific to each data type, and then fusing the data based on relative elevations to achieve accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a unified elevation conversion algorithm is used for all three-dimensional map data, then the processing process is simple, but the alignment accuracy between two-dimensional and three-dimensional map data deteriorates due to varying elevation values of different data types
Solution Approach 1:
The patent applies local quality by selecting different elevation conversion algorithms according to the specific type of three-dimensional map data (line data, surface data, or traffic body data). Each data type receives a tailored algorithm that accounts for its unique elevation characteristics, thereby improving alignment accuracy while maintaining manageable processing complexity through systematic classification.
2Manufacturing precision
If manual correction is performed to align three-dimensional map data with two-dimensional map data, then the alignment accuracy can be improved, but the labor cost and processing time increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform elevation conversion and alignment between two-dimensional and three-dimensional map data. The automated algorithm-based approach eliminates the need for manual correction operations, allowing the system to self-align the data types without human intervention, thereby reducing both labor cost and processing time while maintaining high alignment accuracy.
3Productivity
If existing fusion methods are used without type-specific algorithms, then the processing efficiency is maintained, but the quality of fused map data deteriorates due to inaccurate elevation alignment
Solution Approach 1:
The patent applies parameter changes by modifying the elevation conversion parameters and algorithms based on the specific type of three-dimensional map data being processed. Different data types (line, surface, traffic body) have different elevation parameter characteristics, and the system adjusts the conversion parameters accordingly to ensure accurate alignment, thereby improving data fusion quality while maintaining processing efficiency through automated parameter selection.
Data Source
AI summary
A method for fusing map data, includes: obtaining two-dimensional map data and three-dimensional map data to be fused; classifying the three-dimensional map data according to map data types, the map data types including line data, surface data, and traffic body data; performing elevation conversion for each type of three-dimensional map data according to an elevation conversion algorithm preset for each type of three-dimensional map data, to obtain a relative elevation of each type of three-dimensional map data; and fusing the two-dimensional map data and three-dimensional map data to be fused based on the relative elevation of each type of three-dimensional map data, to obtain fused data.