Tracking Camera Recalibration for Extrinsic Drift Compensation
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Solution Overview
Problem
Existing calibration techniques for input sensors in dynamic environments, such as moving vehicles, fail to account for mechanical and thermal stresses that cause extrinsic drifts, leading to inaccurate spatial reconstruction and compromised user experience in augmented reality systems.
Innovation Solution
A system and method for on-the-fly recalibration of tracking cameras using features from reference and latest images, focusing on non-adjustable parts of the enclosed space, to adjust intrinsic and extrinsic parameters and mitigate positional drifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit calibration processes are performed during manufacturing, then initial sensor accuracy is achieved, but long-term reliability deteriorates due to extrinsic drifts from mechanical stresses and temperature changes
Solution Approach 1:
The system performs preliminary calibration during manufacturing to establish baseline extrinsic parameters, then uses these pre-calibrated parameters as reference for subsequent drift detection and compensation operations throughout the device's operational lifetime
Solution Approach 2:
The system continuously monitors feature positions in captured images, compares them against reference positions from initial calibration, detects drifts, and automatically adjusts extrinsic parameters based on this feedback to maintain long-term accuracy
2Measurement precision
If calibration is performed manually during manufacturing, then initial spatial reconstruction accuracy is achieved, but productivity decreases due to time-consuming calibration processes
Solution Approach 1:
The system performs calibration automatically using its own imaging capabilities and processing units, eliminating the need for manual intervention. The tracking camera captures images, the processor identifies features, calculates drifts, and adjusts parameters autonomously throughout operation
Solution Approach 2:
Instead of performing calibration as a discrete manual step during manufacturing, the system continuously performs calibration-related operations (image capture, feature extraction, drift detection, parameter adjustment) throughout its operational lifetime, transforming calibration from a batch process into a continuous automated process
3Adaptability or versatility
If tracking cameras are used in dynamic environments like moving vehicles, then adaptability to real-world applications is improved, but measurement precision deteriorates due to mechanical stresses and thermal expansion causing extrinsic drifts
Solution Approach 1:
The system transitions from static calibration parameters established during manufacturing to dynamic parameter adjustment. Extrinsic parameters are continuously updated based on real-time drift detection, allowing the system to adapt to changing mechanical and thermal conditions in dynamic environments like moving vehicles
Solution Approach 2:
The system detects changes in extrinsic parameters (position, orientation) caused by mechanical stresses and thermal expansion, then compensates for these parameter changes by adjusting the calibration data to maintain triangulation accuracy despite environmental variations
Data Source
Figure 1~2A
Figure 2B~2C
Figure 3~4
AI summary
A first set of reference images (200) is captured using tracking camera(s) (102, 312). Features (202, 204) are extracted from said first set. A first set of features that pertain to at least non-adjustable part(s) (210a-f) of an enclosed space (206) is selected. Positions of the features of the first set are determined. Latest image(s) is/are captured using the tracking camera(s). Features are extracted from the latest image(s). A second set of features that pertain to at least the non-adjustable part(s) and that match with at least a subset of the first set of features, is selected. Positions of the features of the second set are determined. A difference in a position of a given feature of the second set and a position of a corresponding feature of the first set is determined. The tracking camera(s) is/are calibrated.