Camera Position Estimation via Sensor Fusion and Linear Equations
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Solution Overview
Problem
Existing methods for determining the location of cameras in 3D space from 2D images are computationally intensive and not feasible for real-time processing, especially when dealing with high-resolution data or multiple cameras.
Innovation Solution
A closed-form solution using linear equations is applied, leveraging known parameters from hardware sensors like GPS and accelerometers to efficiently estimate camera positions, reducing the computational burden by expressing the problem as a system of linear equations solvable through standard least squares methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art methods use image matching and optimization to determine camera placement, then measurement precision is improved, but computing time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by using hardware sensors (GPS, accelerometers, gyroscopes) to obtain camera position and orientation data before the image matching process. This pre-acquired sensor data serves as initial constraints that guide the subsequent optimization algorithm, reducing the search space and computational iterations needed to achieve accurate camera placement.
Solution Approach 2:
The patent introduces sensor data as an intermediary element that bridges the gap between 2D image features and 3D camera positioning. Instead of relying solely on complex image matching, the sensor measurements act as a mediator that directly provides camera pose estimates, which then refine the final positioning through a simplified optimization process.
2Manufacturing precision
If prior art methods process all camera parameters (rotation and translation) simultaneously, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the camera parameter estimation problem into two distinct parts: (1) rotation parameters obtained from gyroscope and accelerometer sensors, and (2) translation parameters obtained from GPS and visual matching. This segmentation allows each parameter type to be processed independently using appropriate sensors and algorithms, reducing the overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent applies multi-functionality by using a fused approach that combines data from multiple hardware sensors (GPS for position, accelerometers for orientation, gyroscopes for rotation) with software image matching. This universal system handles both rotation and translation estimation through integrated sensor fusion, simplifying the overall process compared to dedicated separate systems.
3Adaptability or versatility
If prior art methods use only image data without additional sensor parameters, then adaptability is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary acquisition of sensor data (GPS coordinates, accelerometer readings, gyroscope measurements) before initiating image processing. This pre-captured sensor information is then integrated with image matching results, enabling rapid camera positioning without requiring exhaustive image analysis, thus improving processing speed while maintaining adaptability to different environments.
Data Source
AI summary
A method, apparatus, system, article of manufacture, and computer readable storage medium provides the ability to determine two or more camera viewpoint optical centers. A first image and a second image captured by camera devices (and the rotations for the camera devices) are obtained. For each pair of matched points between the first image and the second image, a linear equation is defined that utilizes the rotations, pixel coordinates of the matched points and optical centers. A matrix ATA is computed where each row of A corresponds to a pair of matched points and the number of columns is 3N (N representing the number of camera devices). The matrix is solved resulting in an eigenvector that holds the optical centers that are used to output image information.