Reduced Homography for Camera Pose Estimation
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Solution Overview
Problem
Existing methods for recovering camera pose parameters in three-dimensional environments face challenges due to structural uncertainty and redundancy in image data, particularly when using low-quality cameras and optics, which can lead to inaccurate pose estimation and increased computational complexity.
Innovation Solution
The method employs a reduced homography representation of image data, selecting a subset of image points based on structural uncertainty and redundancy, and applies a reduced homography to estimate pose parameters, allowing for efficient recovery of camera pose even with low-quality sensors and optics by discarding unreliable radial information and focusing on azimuthal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full homography is used to recover camera pose parameters, then measurement precision is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent extracts only the essential azimuthal information from image data while discarding redundant radial information. This is achieved by selecting a reduced set of image points that contain sufficient pose information without the full computational burden of processing all image data, thereby reducing device complexity while maintaining adequate measurement precision.
Solution Approach 2:
The patent changes the parameter representation by using a reduced homography model that operates with fewer parameters than the full homography. By transforming the problem from estimating all pose parameters to estimating a reduced set of parameters that capture the essential motion, the computational complexity is reduced while maintaining measurement precision for the conditioned motion.
2Measurement precision
If all image points are used for pose recovery, then measurement precision is improved, but loss of information is reduced
Solution Approach 1:
The patent extracts and retains only the azimuthal information from image points while discarding redundant radial information. This selective extraction maintains the essential pose-relevant information while eliminating redundant data that would otherwise need to be processed, reducing information loss without sacrificing measurement precision.
Solution Approach 2:
The patent discards redundant radial information that does not contribute to pose estimation accuracy for conditioned motion, while recovering and retaining the essential azimuthal information. This selective discarding and recovering of information reduces the computational burden of processing all image points while maintaining measurement precision.
3Device complexity
If reduced homography is used to reduce computational burden, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies local quality by focusing computational resources on the azimuthal information that is locally relevant to pose estimation for conditioned motion, while discarding radial information that is locally redundant. This selective processing maintains measurement precision for the essential parameters while reducing overall device complexity.
Solution Approach 2:
The patent changes the parameter set from the full homography parameters to a reduced set of parameters that are sufficient for conditioned motion. By identifying and estimating only the parameters that actually affect pose accuracy under the motion constraints, measurement precision is maintained while computational complexity is reduced.
4Loss of information
If full image data is processed, then loss of information is minimized, but productivity decreases due to increased computational time
Solution Approach 1:
The patent extracts only the azimuthal information from image data that is necessary for pose recovery, discarding redundant radial information. This extraction maintains the essential information needed for accurate pose estimation while significantly reducing the amount of data that needs to be processed, thereby improving productivity without excessive information loss.
Solution Approach 2:
The patent discards redundant radial information that does not contribute to pose recovery accuracy, while recovering and retaining the essential azimuthal information. This selective discarding and recovering reduces the computational workload and processing time, improving productivity while maintaining adequate information retention for accurate pose estimation.
Data Source
AI summary
Efficient techniques of recovering the pose of an optical apparatus exploiting structural redundancies due the conditioned motion of an apparatus are disclosed. The techniques are based on determining a reduced homography consonant to the conditioned motion of the optical apparatus. The optical apparatus comprises an optical sensor on which space points are imaged as measured image points. The reduced homography is based on a reduced representation of the space points, obtained by exploiting the structural redundancy in the measured image points due to the conditioned motion. The reduced representation consonant with the conditioned motion is defined by rays in homogeneous coordinates and contained in a projective plane of the optical sensor.