Geospatial Uncertainty Reduction via Reference Marker Intersection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current geospatial location determination methods, such as GPS, suffer from inherent inaccuracies due to high uncertainty in positional measurements, limiting the precision of device location relative to the world and map coordinates.
Innovation Solution
The method involves using reference markers with known physical dimensions and appearance, scanned by a user device, to calculate displacement vectors between markers, iteratively reducing uncertainty radii through translation and intersection analysis, employing SLAM algorithms and augmented reality tools to refine location accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and other geospatial location technologies are used to determine device position, then location information can be obtained, but the measurement precision is limited with uncertainty radii of several meters
Solution Approach 1:
The patent combines multiple independent measurement systems (GPS, Wi-Fi, cellular towers, inertial sensors) into a unified geospatial location determination system. By merging these diverse data sources and processing them through a common uncertainty reduction algorithm that uses reference markers and translation vectors, the system achieves precision beyond what any single technology could provide alone, while managing overall system complexity through integrated processing.
2Measurement precision
If multiple reference markers are scanned and iterative translation operations are performed to reduce uncertainty, then location precision improves to the order of magnitude of the user device, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing a network of reference markers with known precise locations and pre-calculating translation vectors between them. When determining device location, the system only needs to scan these pre-configured markers and apply pre-computed translation operations, rather than performing complex calculations from scratch. This preliminary setup enables rapid iterative uncertainty reduction while maintaining high precision.
3Measurement precision
If iterative translation and intersection analysis is performed to minimize uncertainty radii, then the uncertainty radius is reduced to the order of magnitude of the user device, but the computational operations required increase
Solution Approach 1:
The patent segments the complex uncertainty reduction problem into discrete, manageable operations: (1) scanning individual reference markers, (2) retrieving pre-stored translation vectors for each marker, (3) applying translation operations to uncertainty regions, and (4) computing intersections of translated regions. This segmentation allows the system to process uncertainty reduction in systematic steps rather than as one monolithic complex computation, making the overall process more manageable and efficient.
Data Source
AI summary
Systems and methods for minimizing uncertainty in a geospatial location measurement. First and second positional data of a first reference marker are received, each comprising an uncertainty radius about a preliminary coordinate. A first translation vector comprising the displacement to the first reference marker from the second reference marker is received. The second uncertainty radius is translated by the first translation vector and, based on an intersection area of the translated second uncertainty radius and the first uncertainty radius, the first uncertainty radius is reduced to the intersection area.