Camera Pose Estimation Using Multi-CPU Parallel Processing
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Solution Overview
Problem
Existing methods for estimating the pose of a physical camera in real-time imaging suffer from drifting, loss of track, and low repeatability due to inaccuracies and false matches, making them unsuitable for real-time augmented reality applications.
Innovation Solution
A combination of frame-to-frame tracking with a 3D model and local feature points detection and matching, utilizing a method similar to 'Randomized Trees for Real-Time Keypoint Recognition' for robust estimation, and incorporating motion model observations for numerical minimizations to enhance accuracy and repeatability, while utilizing multi-CPU systems for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame-to-frame tracking is used for real-time camera pose estimation, then processing speed is improved, but accuracy and reliability deteriorate due to drifting and loss of track
Solution Approach 1:
The patent combines frame-to-frame tracking with local feature points detection and matching methods. The tracking component provides fast real-time pose estimation, while the feature matching component (using Randomized Trees) provides accurate and reliable pose recovery. These two methods are merged into a unified system where they complement each other, resolving the contradiction between speed and reliability.
Solution Approach 2:
The patent introduces a 3D model of the environment as an intermediary between the image stream and pose estimation. The 3D model serves as a reference that both the tracking and feature matching methods can utilize, providing a stable foundation for pose estimation and reducing drifting issues while maintaining real-time performance.
2Reliability
If local feature points detection and matching is used for accurate pose estimation, then reliability is improved, but processing speed deteriorates
Solution Approach 1:
The patent pre-computes and stores a 3D model of the environment with known feature points before real-time processing. This preliminary action allows the system to quickly match detected feature points against the pre-built 3D model during real-time operation, achieving both high reliability and fast processing speed without performing computationally intensive 3D reconstruction in real-time.
3Measurement precision
If a 3D model of the environment is used for tracking, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of pose estimation into two independent components: tracking (for speed) and feature matching (for accuracy). The 3D model is used specifically for the feature matching component, allowing accurate pose recovery without requiring the entire system to handle the full complexity of 3D modeling and tracking simultaneously. This segmentation reduces overall system complexity while maintaining high accuracy.
Data Source
AI summary
An apparatus for providing an estimate for a 3D camera pose relative to a scene from 2D image data of a 2D image frame provided by the camera is provided, the apparatus using four types of observations: (a) detected 2D-3D point correspondences; (b) tracked 2D-3D point correspondences; (c) motion model observations; and (d) edge observations.