Structured Lighting 3D Scanner Motion Compensation
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Solution Overview
Problem
Current methods for determining 3D surface position information using structured lighting are not robust enough, particularly when dealing with moving objects, as they often result in identification errors due to object motion and feature invisibility.
Innovation Solution
A method employing a series of structured lighting patterns with intensity features, where the first and second subsets of features are distinguished to minimize identification errors, with motion compensation provided by the second subset, and using complementary lighting patterns to enhance edge detection and 3D position calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single structured lighting pattern is used to illuminate an object, then the system is simple and fast, but identification errors occur when the object moves causing features to become invisible or misidentified
Solution Approach 1:
The lighting pattern is segmented into multiple subsets (first subset and second subset) where each subset contains intensity features at different positions. The first subset provides stable reference features for identification, while the second subset provides additional features for motion compensation, thereby improving reliability without requiring a completely new complex lighting system
Solution Approach 2:
The method performs preliminary identification of intensity features in the first subset to establish reference positions before using the second subset for motion compensation. This preliminary action allows the system to prepare reference data in advance, enabling more reliable identification even when object motion occurs during scanning
2Reliability
If multiple structured lighting patterns are used successively to compensate for object motion, then identification reliability improves, but the scanning time increases
Solution Approach 1:
The method uses a partial approach by dividing the lighting pattern into two subsets with different functions rather than using multiple complete lighting patterns. The first subset provides sufficient reference information for identification, and the second subset adds only the necessary additional features for motion compensation, avoiding the time cost of multiple full pattern illuminations
Solution Approach 2:
The intensity features in the lighting pattern are arranged periodically with regular spacing, allowing the system to identify features based on their periodic characteristics. This periodic structure enables faster feature detection and matching across successive images, reducing the time penalty associated with using multiple lighting patterns
3Measurement precision
If the lighting pattern features are densely distributed to improve edge detection accuracy, then measurement precision improves, but the complexity of distinguishing and identifying features increases
Solution Approach 1:
Different regions of the lighting pattern have different qualities: the first subset provides high-quality stable reference features for reliable identification, while the second subset provides additional features specifically positioned for motion compensation. This local differentiation allows the system to use the appropriate subset for each purpose, improving measurement precision without uniformly increasing complexity across the entire pattern
Solution Approach 2:
The two subsets are designed asymmetrically with different functions and characteristics. The first subset is optimized for stable identification with features that maintain consistent visibility, while the second subset is optimized for motion compensation with features positioned to detect displacements. This asymmetric design simplifies the identification process by clearly distinguishing the role of each feature subset
4Reliability
If motion compensation techniques are applied using reference lighting patterns, then robustness against object motion improves, but the system complexity and processing requirements increase
Solution Approach 1:
The intensity features in the first subset serve as intermediary reference markers that mediate between the lighting pattern and the object surface. These reference features provide a stable intermediate reference frame that simplifies motion compensation by providing clear correspondence points across successive images, reducing the complexity of direct object feature tracking
Solution Approach 2:
The method creates a simplified copy of the object surface geometry by tracking only the intensity features rather than the entire surface. This feature-based copying approach reduces processing complexity by representing the object with discrete marker points instead of continuous surface data, while still providing robust motion compensation
Data Source
Figure 1~3
Figure 2
AI summary
A series of structured lighting patterns are projected on an object. Each successive structured lighting pattern has a first and second subset of intensity features such as edges between light and dark areas. The intensity features of the first set coincide spatially with intensity features from either the first or second subset from a preceding structured lighting pattern in the series. Image positions are detected where the intensity features of the first and second subset of the structured lighting patterns are visible in the images. Image positions where the intensity features of the first subset are visible are associated with the intensity features of the first subset, based on the associated intensity features of closest detected image positions with associated intensity features in the image obtained with a preceding structured lighting pattern in said series. Image positions where the intensity features of the second subset are visible, between pairs of the image positions associated with intensity features of the first subset, with intensity features of the second subset between the intensity features associated with the pair of positions. The associated intensity features in a final structured lighting pattern of the series are used to identify the intensity features of the final structured lighting pattern for the determination of 3D surface position information.