Optical Flow Translation Estimation for Inside-Out Tracking
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Solution Overview
Problem
Conventional team location tracking and mapping systems are ineffective in high-stress, hazardous environments where external location services are unreliable and high-quality visible light images cannot be captured in real time.
Innovation Solution
An inside-out location tracking and mapping system that uses optical flow translation estimation to track camera and scene points in low-contrast and blurry images, without relying on external infrastructure, by optimizing a prior distance parameter and translation vector using a homography model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional outside-in location tracking systems (GPS, cellular) are used, then location tracking works well in urban environments, but they become unreliable in remote locations and hazardous environments
Solution Approach 1:
The patent inverts the conventional outside-in tracking approach by implementing inside-out tracking, where each team member's device independently tracks its own position by capturing images and computing camera motion through optical flow and homography estimation, eliminating dependency on external infrastructure like GPS satellites or cellular towers
Solution Approach 2:
Each team member's location tracking device serves itself by independently performing image capture, feature matching, camera motion estimation, and location computation without requiring external location services, making the system self-sufficient in remote and hazardous environments
2Adaptability or versatility
If known camera-based inside-out location tracking systems are used, then location tracking can be performed without external infrastructure, but they require high-quality visible light images which cannot be captured in real time in hazardous environments
Solution Approach 1:
The patent changes the operational parameters of the imaging system by allowing operation with reduced image quality thresholds, using algorithms that can process low-contrast and blurry images through optimized homography estimation and convergence criteria that adapt to degraded visual conditions
Solution Approach 2:
The system accepts and processes transient, lower-quality images that do not need to meet traditional high-quality standards, treating each frame as a disposable input that can be rapidly processed and discarded in favor of the next frame, enabling real-time tracking in hazardous conditions
3Ease of manufacture
If sparse feature tracking is used, then location tracking can be performed with conventional cameras, but it requires high-quality images for extracting sparse features
Solution Approach 1:
The patent replaces traditional sparse feature extraction mechanisms with a dense optical flow-based approach that uses homography estimation across the entire image plane, substituting the need for distinct sparse features with a continuous field-based method that works effectively with lower image quality
Data Source
AI summary
Techniques for optical flow translation estimation by an inside-out location tracking system may include generating an updated distance parameter by optimizing a distance parameter using a translation vector and a set of other fixed terms of a homography for a set of matched image point pairs in an image, determining whether to keep or to discard the updated distance parameter, optimizing the translation vector using a current distance parameter, either the updated distance parameter or a prior distance parameter that was retained, thereby generating an updated translation vector. An optical flow translation method also may include evaluating for convergence, and iteratively optimizing the distance parameter and the translation vector until there is convergence. Once there is convergence, a current updated translation may be output. Convergence may depend on one or more predetermined thresholds relating to a size of parameter updates, an error reduction, and/or a number of iterations.