Camera Motion Estimation Using Points and Lines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for real-time camera motion estimation using stereo cameras are limited by their reliance on sparse feature sets and lack of efficient unified formulations that utilize both point and line features, leading to challenges in robustness and accuracy.
Innovation Solution
A camera motion system that employs a combination of points and lines detected in visual input data, utilizing a trifocal tensor framework to estimate camera motion through a feature processing module and trifocal motion estimation module, providing a robust and real-time solution by forming first and second trifocal tensors from calibrated camera pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sparse feature sets (points only) are used for camera motion estimation, then real-time processing is achieved, but accuracy and robustness deteriorate
Solution Approach 1:
The patent merges point features and line features into a unified camera motion estimation framework. The system processes both point correspondences and line correspondences simultaneously using a common geometric constraint model, thereby utilizing richer visual information without sacrificing real-time performance. This combination allows the system to achieve higher accuracy while maintaining efficient processing speeds.
Solution Approach 2:
The patent creates a universal camera motion estimation algorithm that handles multiple feature types (points and lines) through a single unified formulation. The geometric constraint model is designed to accommodate both feature types, eliminating the need for separate processing pipelines and enabling the system to leverage the complementary strengths of both point and line features for robust motion estimation.
2Device complexity
If only point features are used, then computational complexity is reduced, but robustness in noisy conditions deteriorates
Solution Approach 1:
The patent combines point features and line features within a unified computational framework that uses common geometric constraints. This merging allows the system to process both feature types with the same algorithmic complexity, avoiding the need for separate complex processing pipelines while gaining the robustness benefits of line features in noisy conditions.
Solution Approach 2:
The patent introduces line-based geometric constraints as additional parameters to the camera motion estimation model. By incorporating line correspondences and their associated geometric constraints alongside point features, the system enriches the parameter space with information that is particularly robust to noise, thereby improving reliability without proportionally increasing computational complexity.
3Measurement precision
If multifocal tensor framework is used with line features, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent develops a universal camera motion estimation algorithm that handles both point and line features through a single formulation based on geometric constraints. This universal approach eliminates the need for separate processing frameworks for different feature types, thereby achieving high measurement precision through line features while avoiding the complexity increase that would result from maintaining separate specialized frameworks.
Data Source
AI summary
A system and method are disclosed for estimating camera motion of a visual input scene using points and lines detected in the visual input scene. The system includes a camera server comprising a stereo pair of calibrated cameras, a feature processing module, a trifocal motion estimation module and an optional adjustment module. The stereo pair of the calibrated cameras and its corresponding stereo pair of camera after camera motion form a first and a second trifocal tensor. The feature processing module is configured to detect points and lines in the visual input data comprising a plurality of image frames. The feature processing module is further configured to find point correspondence between detected points and line correspondence between detected lines in different views. The trifocal motion estimation module is configured to estimate the camera motion using the detected points and lines associated with the first and the second trifocal tensor.