3D Pose Estimation Using Intermediate Reference Frames
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Solution Overview
Problem
Existing computer vision methods struggle with pose estimation in outdoor urban environments, particularly in GPS-challenged areas with poor radio reception, as they fail to effectively combine point and line features for geo-location, leading to noise-prone solutions and limited applicability in real-world scenarios.
Innovation Solution
The method employs intermediate reference frames and geometric constraints like collinearity and coplanarity to reduce polynomial equations from 64th degree to 4th and 8th degree, enabling the solution of minimal pose estimation problems using points and lines, including configurations like 3 points, 2 points and 1 line, 1 point and 2 lines, and 3 lines, which were previously unsolved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional least squares methods are used to solve pose estimation using all available features, then the solution uses all features but becomes noise-prone
Solution Approach 1:
The patent extracts only the minimal necessary subset of features required to solve the pose estimation problem. Instead of using all available features with least squares, the method identifies and uses only the minimal set of point and line correspondences needed to determine camera pose, thereby avoiding noise from redundant features while maintaining solution accuracy.
Solution Approach 2:
The patent applies partial action by using only the minimal necessary features rather than all available features. The minimal solution approach uses exactly the number of features needed to solve the geometric problem (e.g., 3 points, 2 points + 1 line, 1 point + 2 lines, or 3 lines) without the excessive use of all possible features, reducing noise impact while maintaining computational efficiency.
2Reliability
If minimal solutions are used for pose estimation, then noise resistance improves but the ability to handle various feature configurations is limited
Solution Approach 1:
The patent creates a universal minimal solution framework that can handle multiple feature configurations (3 points, 2 points + 1 line, 1 point + 2 lines, 3 lines) through a unified mathematical approach. The method uses a common algebraic formulation with intermediate reference frames that adapts to different minimal configurations, making the solution both noise-resistant and versatile across various feature types.
Solution Approach 2:
The patent changes parameters by transforming the pose estimation problem into different coordinate systems (intermediate reference frames) and adjusting the mathematical formulation to accommodate different minimal configurations. This allows the same minimal solution approach to work across multiple feature types by changing the parameter representation rather than requiring separate solutions for each configuration.
3Manufacturing precision
If 64th degree polynomial equations are solved for pose estimation, then complete geometric constraints are applied but computational complexity increases
Solution Approach 1:
The patent segments the high-degree polynomial problem into lower-degree subproblems by introducing intermediate reference frames. Instead of solving a single 64th degree polynomial, the method divides the problem into sequential steps working in intermediate coordinate systems, reducing the computational burden while maintaining the accuracy of geometric constraints through systematic decomposition.
Solution Approach 2:
The patent introduces intermediate reference frames as mediators between the image coordinate system and the world coordinate system. These intermediate frames serve as computational bridges that simplify the mathematical relationships, allowing geometric constraints to be applied accurately through a series of lower-complexity transformations rather than a single high-degree polynomial solution.
Data Source
AI summary
A three-dimensional (3D) pose of a 3D object in an environment is determined by extracting features from an image acquired of the environment by a camera. The features are matched to a 3D model of the environment to determine correspondences. A camera reference frame of the image and a world reference frame of the environment are transformed to a corresponding intermediate camera reference frame and a corresponding world reference frame using the correspondences. Geometrical constraints are applied to the intermediate camera reference frame and the intermediate world reference frame to obtain a constrained intermediate world reference frame and a constrained world reference frame. The 3D pose is then determined from parameters of the constrained intermediate world reference frame and the constrained world reference frame.