3D Acquisition System Dynamic Calibration via Sensor Crossing Points
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Solution Overview
Problem
Conventional machine vision systems lack efficient methods for dynamic calibration of 3D acquisition systems, leading to laborious and time-consuming processes that often require manual interventions, especially in environments with varying conditions and applications.
Innovation Solution
A sensing system that employs multiple signal beams to scan paths across objects, with sensors detecting reflected signals to determine crossing points and event metrics, allowing for automatic calibration by synchronizing sensor data and adjusting position and rotation angles for precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used for 3D acquisition systems, then calibration accuracy can be achieved, but the process becomes laborious and time-consuming
Solution Approach 1:
The system performs automatic self-calibration by capturing images of the calibration pattern, detecting feature points, and computing calibration parameters without human intervention. The calibration process is automated through software algorithms that process the captured images and adjust system parameters automatically.
Solution Approach 2:
The calibration pattern is pre-designed with specific geometric features that facilitate automatic detection and measurement. The known geometry of the calibration pattern allows the system to pre-compute reference points and use them for rapid calibration without requiring manual measurement during the calibration process.
2Reliability
If manual calibration interventions are required, then calibration can be performed, but the complexity and labor requirements increase
Solution Approach 1:
The system automatically performs calibration through software algorithms that process captured images and compute calibration parameters without human intervention, eliminating the need for manual calibration operations and reducing process complexity.
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated optical and software-based systems. Instead of physical adjustment and manual measurement, the system uses digital image processing and computational algorithms to achieve calibration, thereby reducing mechanical complexity and labor requirements.
3Adaptability or versatility
If conventional calibration methods are used, then basic calibration can be achieved, but dynamic adaptation to varying environments is limited
Solution Approach 1:
The calibration system is designed to be dynamic and adaptive, automatically adjusting to varying environmental conditions such as changes in lighting, temperature, or system configuration. The software can re-calibrate the system in response to environmental changes without requiring complete re-calibration, maintaining adaptability while preserving calibration efficiency.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on environmental conditions and system state. By monitoring environmental factors and automatically modifying calibration parameters, the system maintains accuracy across varying conditions without sacrificing calibration speed or requiring extensive re-calibration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and accurate dynamic calibration of 3D acquisition systems, improving their performance across different environments and applications without the need for manual intervention, enhancing the precision and efficiency of machine vision applications.
Implementation Method 1
sensors detecting reflected signals to determine crossing points and event metrics
Data Source
AI summary
Embodiments are directed to a sensing system that employs beams to scan paths across an object such that sensors may the beams reflected by the scanned object. Events may be provided based on the detected signals and the paths such that each event may be associated with a sensor and event metrics. Crossing points for each sensor may be determined based on where the paths intersect the scanned object such that events associated with each sensor are associated with the crossing points for each sensor. Each crossing point of each sensor may be compared to each correspondent crossing point of each other sensor. actual crossing points may be determined based on the comparison and the crossing points for each sensor. Position information for each sensor may be determined based on the actual crossing points.