Camera Pose Estimation via Scale Re-projection Error
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera pose estimation methods in computer vision, such as bundle adjustment optimization, face challenges in maintaining accuracy over time, especially in environments without GPS, due to accumulation of estimation errors and rapid trajectory drift, especially when re-observing previously mapped areas or continuously exploring new areas.
Innovation Solution
Incorporating image feature scale information into the bundle adjustment optimization calculation using scale re-projection error terms and sparsity-aware iterative optimization techniques, such as g2o and Ceres Solver, to improve the accuracy of camera pose estimation by formulating scale constraints and using Scale Invariant Feature Transform (SIFT) for feature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bundle adjustment optimization is used for camera pose estimation, then pose estimation can be performed, but estimation errors accumulate over time causing trajectory drift
Solution Approach 1:
The patent incorporates scale information from image features as feedback into the bundle adjustment optimization process. By continuously measuring and utilizing the scale of detected image features (landmarks) across multiple images, the system provides corrective feedback that prevents error accumulation and trajectory drift, thereby maintaining both estimation accuracy and temporal reliability
Solution Approach 2:
The patent introduces scale information as an intermediary element between image features and pose estimation. This intermediate scale measurement acts as a mediator that constrains the optimization process, linking observations across different images and preventing the accumulation of errors that would otherwise occur in standard bundle adjustment
2Measurement precision
If scale information is incorporated into bundle adjustment optimization, then estimation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameters of the bundle adjustment optimization by incorporating scale information as an additional constraint. This parameter change enhances estimation accuracy by adding scale re-projection error terms to the optimization cost function, while the sparsity-aware techniques manage the resulting computational complexity
3Productivity
If sparsity-aware iterative optimization techniques are used, then computational efficiency improves, but convergence difficulty increases in challenging environments
Solution Approach 1:
The patent applies preliminary actions by using sparsity-aware iterative optimization techniques that pre-process and structure the optimization problem before full computation. This preliminary structuring of the sparse optimization problem improves computational efficiency while maintaining convergence reliability even in challenging environments with low-contrast scenes and short track-life landmarks
Data Source
AI summary
A method for estimating a plurality of camera, comprising using at least one processor executing a code for: extracting a plurality of image features of a plurality of landmarks from a plurality of images captured by at least one camera from at least one pose, the plurality of landmarks calibrated with respect to a certain coordinate system; identifying among the plurality of image features at least one image feature documented in at least some of the images; producing scale values of at least one common image feature by analyzing the at least some of the images; determining a plurality of estimated poses of the at least one camera with respect to the certain coordinate system by using the scale values in calculating a minimal re-projection error between the plurality of image features and a plurality of predicted image features; and outputting the plurality of estimated poses.