Handheld Dimensioning System Depth Map Null Pixel Feedback
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Solution Overview
Problem
Handheld dimensioning systems face challenges in accurately measuring object dimensions due to uncontrolled measurement environments with varying lighting, geometries, and object colors, leading to potential measurement errors and inefficiencies.
Innovation Solution
A handheld dimensioning system that analyzes depth map data to determine the null-data pixel count, providing feedback through confidence or user-guidance messages to ensure accurate measurements by adjusting lighting or geometry to meet quality thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If handheld dimensioning is used to enable mobile measurement in varied environments, then ease of operation and adaptability improve, but measurement precision deteriorates due to uncontrolled lighting, geometry, and object color conditions
Solution Approach 1:
The system analyzes the depth map to determine a null-data pixel count and compares it against a threshold. When the null-data pixel count exceeds the threshold, the system generates error feedback to alert the user of poor measurement conditions. This feedback mechanism enables users to adjust their measurement approach in real-time, thereby maintaining measurement precision while preserving the mobility advantage of handheld operation.
2Adaptability or versatility
If measurement is performed in uncontrolled environments with varying lighting and object colors, then adaptability improves, but measurement precision deteriorates due to adverse environmental factors
Solution Approach 1:
The system evaluates environmental quality by analyzing null-data pixels in the depth map and provides feedback when conditions are adverse. This allows the system to adapt to various environments while maintaining measurement precision by alerting users to problematic conditions such as poor lighting or unsuitable object surfaces.
Solution Approach 2:
The system uses a threshold parameter for null-data pixel count to determine whether measurement conditions are acceptable. By monitoring and comparing this parameter against the threshold, the system can dynamically assess environmental suitability and guide users to adjust measurement parameters or conditions to maintain precision.
3Measurement precision
If null-data pixel analysis is performed to improve measurement quality, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs null-data pixel analysis and provides feedback only when the null-data pixel count exceeds the threshold. This selective feedback approach improves measurement precision by identifying poor quality measurements while avoiding unnecessary processing complexity for already good measurements, thus optimizing the balance between precision and computational burden.
4Measurement precision
If error feedback is generated for poor quality depth maps, then measurement precision improves through error reduction, but productivity decreases due to additional verification steps
Solution Approach 1:
The system generates error feedback selectively only when the null-data pixel count exceeds the threshold, rather than for every measurement. This selective feedback mechanism improves measurement precision by identifying and flagging only the problematic measurements, thereby minimizing the impact on productivity while still ensuring accuracy for measurements that meet quality standards.
Data Source
AI summary
A dimensioning system that analyzes a distance map for null-data pixels to provide feedback is disclosed. Null-data pixels correspond to missing range data and having too many in a distance map may lead to dimensioning errors. Providing feedback based on the number of null-data pixels helps a user understand and adapt to different dimensioning conditions, promotes accuracy, and facilitates handheld applications.