3D Vision Cuboidal Object Dimension Refinement
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Solution Overview
Problem
Existing 3D vision systems face challenges in accurately measuring the dimensions of cuboidal objects on a conveyor due to noise and coarse resolution, which limits their ability to meet tight accuracy requirements, especially when objects have varying sizes and shapes.
Innovation Solution
A system and method that employs statistical analysis to refine the dimensions of cuboidal objects by analyzing 3D image data, using techniques such as Points Statistical Analysis, Boundary Statistical Analysis, Height from Least Squares Surface Fitting, and Height from Bulginess, to provide accurate length, width, and height measurements, while also accounting for object bulginess and convexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct use of bounding box dimensions from 3D point cloud is employed, then measurement process is simple, but measurement precision is insufficient for tight accuracy requirements
Solution Approach 1:
The measurement process is segmented into multiple distinct stages: initial bounding box detection, statistical analysis of boundary points, identification of extreme points along each dimension, and refinement calculations. This segmentation allows each stage to focus on specific aspects of measurement, improving overall precision while maintaining manageable complexity through modular processing.
Solution Approach 2:
The system performs preliminary statistical analysis on the 3D point cloud data before final dimension calculation. By pre-identifying boundary points, calculating statistical parameters, and detecting extreme points in advance, the system prepares refined data structures that enable more accurate final measurements without requiring complex real-time processing.
2Measurement precision
If statistical analysis methods are applied to refine dimensions, then measurement precision improves, but processing time increases
Solution Approach 1:
The statistical analysis is applied locally to specific regions and aspects of the object rather than processing the entire point cloud uniformly. The system identifies and analyzes boundary points, extreme points, and facial features in localized areas, which reduces the overall computational burden while maintaining measurement precision for the critical dimensions.
Solution Approach 2:
The system performs statistical analysis selectively on the most relevant points and regions that contribute to dimension measurement, rather than analyzing all points in the point cloud. By focusing computational resources on boundary points and extreme points that directly affect dimension accuracy, the system achieves high precision without unnecessary processing of redundant data.
3Adaptability or versatility
If 3D vision system operates on moving objects with varying sizes, then adaptability improves, but measurement consistency deteriorates due to noise
Solution Approach 1:
The system dynamically adjusts measurement parameters and statistical thresholds based on the detected object characteristics. By changing analysis parameters according to object size, shape, and position, the system maintains consistent measurement quality across varying object types while adapting to different scenarios, thereby improving both adaptability and reliability.
Solution Approach 2:
The measurement process incorporates feedback mechanisms where statistical analysis results inform subsequent processing steps. The system uses detected boundary points and extreme points to refine dimension calculations, and adjusts processing parameters based on measured object properties, creating a closed-loop system that maintains consistency despite variations in object characteristics and noise conditions.
Data Source
AI summary
A system and method for estimating dimensions of an approximately cuboidal object from a 3D image of the object acquired by an image sensor of the vision system processor is provided. An identification module, associated with the vision system processor, automatically identifies a 3D region in the 3D image that contains the cuboidal object. A selection module, associated with the vision system processor, automatically selects 3D image data from the 3D image that corresponds to approximate faces or boundaries of the cuboidal object. An analysis module statistically analyzes, and generates statistics for, the selected 3D image data that correspond to approximate cuboidal object faces or boundaries. A refinement module chooses statistics that correspond to improved cuboidal dimensions from among cuboidal object length, width and height. The improved cuboidal dimensions are provided as dimensions for the object. A user interface displays a plurality of interface screens for setup and runtime operation.