Camera-Based 3D Coordinate Approximation via Radial Convolution
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Solution Overview
Problem
Conventional methods for approximating the location of a point of interest in a three-dimensional coordinate system, such as triangulation, are time-consuming and provide only rudimentary accuracy, often requiring significant resources and taking up to 20 minutes to establish coordinates with an accuracy of around +/-300 meters.
Innovation Solution
A method involving a camera-oriented system that retrieves camera data, including heading and tilt, and compares it with topographic map data to identify matching coordinates, allowing for rapid and accurate approximation of a point's location, which can be displayed on a graphic user interface, and optionally directs cameras for optimal viewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangulation techniques are used to establish map coordinates, then measurement precision is improved to approximately +/-300 meters, but loss of time increases to 20 minutes or longer
Solution Approach 1:
The patent replaces the manual mechanical triangulation process with an automated optical-electronic system. A camera captures images of the point of interest, and computer vision algorithms automatically process the images to determine coordinates, eliminating the need for manual triangulation operations and reducing time from 20+ minutes to seconds while maintaining accuracy.
Solution Approach 2:
The patent creates a digital copy of the physical scene through camera imaging. Instead of direct manual measurement, the system captures an optical copy (image) of the point of interest and processes it through algorithms to extract coordinate information, enabling rapid automated analysis without physical intervention.
2Measurement precision
If triangulation techniques are used to establish map coordinates, then measurement precision is improved to approximately +/-300 meters, but device complexity increases due to requiring multiple overlapping views and manual translation processes
Solution Approach 1:
The system performs automatic self-processing through automated image capture and algorithmic coordinate extraction. The camera and processing software work together to automatically determine coordinates without requiring manual intervention for view selection, angle measurement, or coordinate translation, thereby reducing operational complexity.
Solution Approach 2:
The patent extracts only the essential information needed for coordinate determination from the captured image. Instead of requiring multiple overlapping views and complex manual triangulation, the system extracts key features and coordinates directly from a single or minimal set of images through automated processing.
3Quantity of substance
If conventional triangulation methods are used, then resource requirements increase due to manual operations and multiple views, but productivity remains low with results taking 20 minutes or longer
Solution Approach 1:
The patent replaces manual mechanical triangulation operations with automated optical capture and electronic processing. The camera and computer algorithms substitute for human operators performing manual measurements and calculations, dramatically reducing both resource requirements and processing time to seconds.
Solution Approach 2:
The system enables continuous automated operation where the camera can continuously capture images and the processing algorithms continuously extract coordinates. This continuous automated process eliminates idle time between manual operations and maintains high productivity throughout the measurement process.
Data Source
AI summary
The present invention provides a method for approximating the location of a point of interest relative to a three dimensional coordinate system, the method including the steps of: orientating a camera toward the point of interest such that the point of interest can be seen within a camera view; retrieving camera data including a location relative to the three dimensional coordinate system, and a camera position including a camera heading and a camera tilt; querying an associated topographic map database to identify one or more coordinates located on the topographic map along the camera heading to form a first dataset; computing one or more coordinates located on a radial plane between the camera and the point of interest based on the camera heading and camera tilt to form a second dataset; and comparing the first dataset with the second dataset to identify a pair of matching coordinates; wherein the matching coordinates represent the three dimensional coordinate location of the point of interest. A system and software for performing the method is also provided.