Visual Sensor Network Calibration Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-sensor-based global positioning systems face challenges in maintaining valid calibration, as tiny drifts in sensor position or rotation significantly degrade positioning accuracy, and current validation methods require precise calibration targets and expert knowledge, making regular validation difficult.
Innovation Solution
An apparatus and method for automatically validating the calibration of a visual sensor network with multiple calibrated visual sensors by extracting geometric relationships from image data, comparing these with previous calibration information, and converting results into an indication of calibration validity without relying on visible fiducial markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration validation methods are used with fiducial markers and expert knowledge, then measurement precision is maintained, but device complexity and ease of operation deteriorate due to requiring precise manufactured targets and expert intervention
Solution Approach 1:
The system performs self-validation by automatically comparing current geometric relationships between sensors with previously stored calibration data. The sensor network validates its own calibration status without requiring external expert intervention or specially manufactured fiducial markers, thereby maintaining measurement precision while dramatically improving ease of operation
Solution Approach 2:
The invention extracts and removes the dependency on external fiducial markers and expert knowledge from the validation process. By using naturally occurring environmental features and automated image processing, the system eliminates these external dependencies while maintaining calibration validation accuracy
2Reliability
If frequent calibration validation is performed to detect drift, then reliability is improved, but loss of time increases due to manual validation processes
Solution Approach 1:
The system enables continuous or frequent calibration validation by automating the entire validation process. Multiple visual sensors continuously capture images and the processor automatically compares geometric relationships with stored calibration data, allowing frequent reliability checks without significant time loss compared to manual methods
Solution Approach 2:
The invention replaces manual mechanical validation processes with automated optical and computational systems. Image processing algorithms and automated geometric calculations substitute for manual measurement and expert analysis, enabling frequent validation while minimizing time loss
3Ease of operation
If automated validation without fiducial markers is implemented, then ease of operation is improved, but measurement precision may deteriorate without precise calibration targets
Solution Approach 1:
The system uses multiple visual sensors that can capture and process various types of environmental features (natural landmarks, artificial structures, text regions) as calibration references. This multi-functionality allows the system to maintain measurement precision using diverse environmental features instead of requiring specific fiducial markers, while keeping the operation simple and automated
Data Source
AI summary
Automatically validating the calibration of an visual sensor network includes acquiring image data from visual sensors that have partially overlapping fields of view, extracting a representation of an environment in which the visual sensors are disposed, calculating one or more geometric relationships between the visual sensors, comparing the calculated one or more geometric relationships with previously obtained calibration information of the visual sensors, and verifying a current calibration of the visual sensors based on the comparison.