Tracking Camera Recalibration Using Fixed-Space Feature Drift
Find Innovative SolutionsGenerate Solutions
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 the difference between feature positions in reference and latest images, focusing on non-adjustable parts of the enclosed space to mitigate positional drifts and improve accuracy.
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 and thermal stresses
Solution Approach 1:
The system performs preliminary calibration during manufacturing to establish baseline extrinsic parameters, then proactively detects and corrects drifts before they significantly degrade performance. The recalibration process is initiated based on detected changes in the environment or accumulated drift thresholds, preventing accuracy loss rather than reacting after failure occurs.
Solution Approach 2:
The system continuously monitors feature positions in captured images and compares them against reference data to detect extrinsic drifts. This feedback loop enables real-time identification of calibration degradation, triggering recalibration operations when drift exceeds acceptable thresholds, thus maintaining long-term reliability through active correction.
2Measurement precision
If recalibration is performed frequently to maintain accuracy, then measurement precision is improved, but loss of time increases due to calibration operations
Solution Approach 1:
Instead of performing full recalibration operations frequently, the system performs partial recalibration only when and where needed. It selectively updates extrinsic parameters based on detected drift patterns and operational context, applying correction only to affected camera sensors or parameter sets, thereby minimizing time loss while maintaining necessary accuracy.
Solution Approach 2:
The system performs lightweight preliminary checks continuously in the background to monitor for drift conditions. When drift is detected, it prepares recalibration data in advance and executes corrections during natural pauses in operation or transitions, minimizing disruption to primary tasks and reducing perceived calibration time.
3Reliability
If on-the-fly recalibration is implemented, then long-term reliability is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs self-calibration using its own captured images and onboard processing capabilities. It automatically detects drifts, computes correction parameters, and applies recalibration without external intervention or complex additional hardware, achieving improved reliability through self-sufficient operations that minimize added system complexity.
Solution Approach 2:
The recalibration system leverages existing camera sensors and processors for multiple purposes: capturing images for primary tasks, detecting drift through feature analysis, computing calibration corrections, and applying updates. This multi-functional use of existing components achieves reliable on-the-fly recalibration without adding dedicated complex calibration hardware or processing systems.
Data Source
AI summary
A first set of reference images is captured using tracking camera(s). Features are extracted from the first set. A first set of features that pertain to at least non-adjustable part(s) of an enclosed space 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.