3D Feature Localization Using Pre-Calibrated Sensor Frame Mapping
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Solution Overview
Problem
Existing depth-sensing camera systems face inaccuracies and high computational burdens due to bright light emission and noise in environments like automated welding, making them unsuitable for high-accuracy applications such as additive manufacturing.
Innovation Solution
A calibration method that maps a three-dimensional reference system to the frames of multiple image sensors, allowing for precise depth sensing without requiring specific sensor positioning or focal length measurements, reducing computational load and noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth-sensing camera systems use triangulation based on signal reflection or stereo-vision with deep learning algorithms, then depth information can be obtained, but the systems suffer from high computational burden and slow determination speed
Solution Approach 1:
The patent applies preliminary action by pre-calibrating the sensing system to create a mapping between image sensor positions and three-dimensional reference positions before actual depth sensing operations. This pre-established mapping eliminates the need for complex real-time computations during actual use, thereby resolving the contradiction between measurement precision and determination speed.
2Measurement precision
If depth-sensing camera systems use stereo-vision or signal reflection methods, then depth information can be obtained, but the systems have high computational burden especially in noisy environments
Solution Approach 1:
The patent replaces complex computational algorithms with a pre-established mapping table that directly correlates image sensor positions to three-dimensional reference positions. This substitution of computational mechanics with a lookup-based approach significantly reduces the computational burden while maintaining depth sensing capability.
3Measurement precision
If traditional depth-sensing systems are used in automated welding environments, then depth information can be captured, but the systems produce inaccurate results due to bright light emission and noise
Solution Approach 1:
The patent changes the operational parameters of the sensing system by using a single image sensor with pre-calibrated mapping instead of multi-sensor systems that are sensitive to environmental conditions. This parameter change makes the system more robust against noise from welding light while maintaining measurement accuracy.
Data Source
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AI summary
There is provided a method (300) of calibrating a sensing system (100) for determining a position of a feature (236) of an object (10). The method (300) comprises: defining (310) reference positions (181, 182) within a three-dimensional reference system; determining (320, 330) corresponding positions within an image frame (122) of an image sensor (120) and positions within a sensor frame (132) of a further sensor (130); and generating (340) a mapping (150) linking the reference positions and the positions. A position of the feature of the object may be determined based on the mapping. There is also provided a method (400) for monitoring a workpiece (10), the method (400) comprising: obtaining (420, 430) shortwave infrared images (125, 135); determining (440) a corresponding image position of a feature of the workpiece or of an automated welding system using a machine-learned model (190); and determining (450) a position of the feature.