Image Capture Position Estimation Using Depth Analysis
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Solution Overview
Problem
Existing image capture position and direction estimation methods require accurate camera parameter calibration and GPS-based landmark positioning, which can lead to decreased accuracy when these conditions are not met, and rely on dense image databases that are time-consuming to generate and may result in reduced self-position estimation accuracy as the capture distance increases.
Innovation Solution
An image capture position and direction estimation device that determines regions between a captured image and a reference image, estimates depth information for these regions, and uses this information to estimate the image capture position and direction without the need for specific landmark or camera parameter data, allowing for accurate estimation even when the capture position is distant from the reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based landmark positioning and camera parameter calibration are used, then position estimation accuracy is improved, but device complexity and operational requirements increase
Solution Approach 1:
The patent extracts and removes the dependency on external systems (GPS, landmark databases, camera calibration) from the position estimation process. It uses only the captured image data itself, extracting geometric relationships directly from the image to estimate position and orientation without requiring any external positioning infrastructure or pre-calibrated parameters.
Solution Approach 2:
The system performs self-position estimation using only its own captured image and internal processing. It does not rely on external services like GPS or pre-existing landmark databases. The method uses the image geometry itself to derive position and orientation information, making the system self-sufficient and independent of external positioning infrastructure.
2Measurement precision
If dense image databases with assigned positions and directions are used, then position estimation accuracy is improved, but time consumption for database generation increases
Solution Approach 1:
The patent removes the requirement for pre-generated dense image databases entirely. Instead of using extensive databases with manually or computationally assigned positions and directions, it extracts position and orientation information directly from geometric relationships within a single captured image, eliminating the time-consuming database generation process.
Solution Approach 2:
The method performs position and orientation estimation in real-time from a single image capture, eliminating the need for preliminary database generation. The system processes the image immediately upon capture and derives position information without requiring pre-computed reference databases, thus removing the time loss associated with database creation.
3Ease of operation
If panorama image matching is used, then position estimation is simplified, but accuracy deteriorates as capture distance from reference image increases
Solution Approach 1:
The patent transitions from two-dimensional panorama image matching to three-dimensional geometric relationship analysis. By using depth information and spatial geometry from multiple detected objects in the image, it creates a 3D position estimation model that remains accurate regardless of distance from reference images, overcoming the distance limitation of 2D matching methods.
Solution Approach 2:
The method changes the estimation parameters from simple 2D image coordinate matching to 3D spatial geometric relationships. By incorporating depth information and using geometric constraints from multiple objects, it transforms the estimation problem into one that is insensitive to distance from reference images, maintaining accuracy across varying capture distances.
Data Source
AI summary
An image capture position and direction estimation device includes a region determination unit that determines a plurality of regions to be associated between a query image and an image with position and direction, a depth estimation unit that estimates a depth of each region, and an image capture position and direction estimation unit that estimates a direction in the region with a large depth estimated by the depth estimation unit and estimates a position in the region with a small depth estimated by the depth estimation unit.