Multi-match Likelihood Function for Pose Estimation Accuracy
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Solution Overview
Problem
Existing image processing methods face challenges in accurately estimating pose due to occlusions, lighting differences, and motion blur, leading to multiple mismatches and increased computational resource requirements, especially in real-time image capture scenarios.
Innovation Solution
The method employs a multi-match likelihood function to identify the closest 2D feature point among multiple possible matches for each 3D feature point, allowing for simultaneous feature matching and pose estimation, which adaptsively selects the best matches to capture a larger set of inliers and avoids local minima through a sampling-based framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feature matching methods are used to estimate pose, then the process is computationally simpler, but matching accuracy deteriorates due to occlusions, lighting differences, and motion blur causing multiple mismatches
Solution Approach 1:
The patent segments the feature matching process into multiple stages: initial feature detection, candidate match generation, inlier identification through RANSAC, and final pose computation. This segmentation allows each stage to focus on specific tasks, improving overall accuracy while managing computational load through progressive refinement rather than attempting all matching simultaneously
Solution Approach 2:
The patent performs preliminary feature detection and candidate match generation before final pose estimation. By pre-identifying potential feature correspondences and filtering candidates based on geometric constraints beforehand, the system reduces the search space for the final pose computation, thereby improving accuracy without proportionally increasing overall computational complexity
2Measurement precision
If simultaneous feature matching and pose estimation are performed, then matching accuracy improves by capturing more inliers, but computational resource requirements increase significantly
Solution Approach 1:
The patent performs partial simultaneous matching by considering multiple candidate matches for each feature point rather than a single match. This partial action approach captures more potential inliers by evaluating multiple hypotheses simultaneously, while the excessive action of computing all candidates is tempered by subsequent filtering through RANSAC and geometric constraint validation, which eliminate false matches without requiring full exhaustive computation
3Reliability
If multiple candidate matches are evaluated for each 3D feature point, then pose estimation robustness improves, but the number of possible matches increases exponentially
Solution Approach 1:
The patent applies local quality by evaluating match candidates independently for each 3D feature point based on local geometric constraints and descriptor similarity. Each feature point's candidate matches are assessed using local information (neighborhood geometry, feature descriptors) rather than requiring global evaluation of all match combinations. This local assessment approach maintains robustness by considering multiple candidates while avoiding exponential complexity through localized constraint application
Solution Approach 2:
The patent transforms the combinatorial matching problem into a geometric constraint satisfaction problem by evaluating matches in the spatial dimension. Instead of combinatorially evaluating all possible match combinations, the system uses 3D geometric constraints (epipolar geometry, point-to-line distances, reprojection errors) to filter and rank candidates. This dimensional transformation from combinatorial space to geometric space reduces complexity while maintaining robustness through spatial constraint validation
Data Source
AI summary
There is provided a method for estimating a pose of an image in a spatial region and an image processing arrangement. The image processing arrangement is coupled to an imaging capturing arrangement configured to capture one or more 2-D images of a scene within the spatial region. The image processing arrangement is provided with spatial coordinates of a plurality of 3-D point locations within the spatial region. The method includes identifying key features present in 2-D images; identifying a correspondence of clusters of the plurality of 3-D point locations to key feature points present in the 2-D images; and from the identified correspondence, estimating the likely pose of the image processing arrangement relative to the spatial region by using a multi-match likelihood function that finds for each 3-D point location a closest 2-D feature point among k possible 2-D key feature points, where k is an integer greater than one.