Landmark Configuration Matching for Unknown-Camera Geolocation
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Solution Overview
Problem
Existing methods for determining location from images taken with unknown camera parameters are time-consuming and rely on manual association with landmarks, which is impractical for large areas with common features.
Innovation Solution
A method that utilizes a Geolocation Knowledge Base (GKB) combining global data into grid cells, allowing for efficient comparison of image and camera characteristics with region-specific data to quickly identify likely geographic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual landmark association methods are used to determine location from images, then measurement precision can be achieved, but the process becomes time-consuming and impractical for large areas
Solution Approach 1:
The patent segments the large geographic area into a grid of smaller cells, each with pre-computed landmark configurations. This allows the system to quickly search through pre-processed data rather than performing manual landmark association across the entire large area, thus reducing time while maintaining precision through systematic grid-based comparison
Solution Approach 2:
The patent performs preliminary computation of landmark configurations and stores them in a database before actual location determination is needed. This pre-processing step creates ready-to-use reference data that can be quickly compared against image features during runtime, eliminating the need for time-consuming manual landmark association when actual location determination is performed
2Measurement precision
If comprehensive global data is analyzed to determine location, then measurement precision improves, but data processing complexity and time increase significantly
Solution Approach 1:
The patent divides comprehensive global data into a grid structure where each cell contains only the landmark configurations relevant to that specific geographic region. This segmentation reduces the amount of data that needs to be processed at any given time while maintaining comprehensive coverage, as the system only needs to query and compare data from the specific grid cell containing the target location
Solution Approach 2:
The patent creates region-specific data for each grid cell that is optimized for local geographic characteristics. Each cell's data contains landmark configurations tailored to that specific region, allowing the system to use locally-optimized data structures and comparison methods that reduce processing complexity while maintaining high precision for local location determination
3Measurement precision
If manual resection methods are used for location determination in large areas, then measurement precision can be maintained, but productivity decreases due to time-consuming processes
Solution Approach 1:
The patent replaces the manual mechanical process of resection with an automated computer-based system that uses image processing and database comparison. The system automatically extracts features from images, compares them against pre-computed landmark configurations in the database, and determines location through algorithmic matching, thereby eliminating the need for manual resection operations while maintaining measurement precision
Solution Approach 2:
The patent changes the operational parameters from manual angle measurements and geometric constructions to automated image feature extraction and database matching. By transforming the problem into a parameter-based comparison task using pre-computed landmark configurations, the system achieves both high productivity through automation and maintained precision through systematic comparison methods
Data Source
AI summary
Systems and techniques for determining a list of geographic location candidates from an image of an environment are described. Open source data indicative of the earth's surface may be obtained and combined into grids to create region data to gain a representation of the earth's surface within each grid cell. An image of an environment may be obtained from an unknown camera. Image characteristics may be compared to the region data to determine error between the image characteristics and projections of the region data. A list of lowest error geographic location candidates may be determined and provided to the user.