Gap Detection in Support Structures Using Peg and Shelf Classification
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Solution Overview
Problem
In environments like retail and distribution facilities, the varying structural features of support structures can reduce the accuracy of product status determination by mobile automation systems, which rely on capturing data to identify product status information.
Innovation Solution
A method and apparatus that use depth measurements and label indicators to classify labels as peg or shelf labels, generating an item search space to determine the presence of items on support structures, enabling accurate detection of gaps and product status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile automation systems use general data capture methods to identify product status information, then the system can operate across various support structure types, but the accuracy of product status determination is reduced due to varying structural features
Solution Approach 1:
The system segments the support structure into distinct regions (peg regions and non-peg regions) based on depth measurements. By dividing the search space into these segments, the system can apply region-specific detection logic, improving accuracy while maintaining adaptability across different support structure types
Solution Approach 2:
The system applies different detection criteria and search strategies to different regions of the support structure. Peg regions use one set of detection parameters while non-peg regions use another, allowing the system to optimize for local structural characteristics while operating across diverse support structure types
2Measurement precision
If the system uses depth measurements and label classification to distinguish peg regions from shelves, then gap detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The system uses the existing depth measurement data and label indicators already captured by the mobile automation apparatus to automatically classify regions and detect gaps. The classification process leverages the inherent structural information in the depth data without requiring additional sensors or manual configuration, improving accuracy while limiting complexity growth
Solution Approach 2:
The system performs preliminary classification of label indicators as peg or shelf labels before conducting gap detection. This preliminary action organizes the detection process and enables more accurate gap identification by pre-establishing region boundaries based on depth measurements and label positions
Data Source
AI summary
A method of detecting gaps on a support structure includes: obtaining, at an imaging controller, (i) a plurality of depth measurements representing the support structure according to a common frame of reference, and (ii) a plurality of label indicators each defining a label position in the common frame of reference; for each of the label indicators: classifying the label indicator as either a peg label or a shelf label, based on a portion of the depth measurements selected according to the label position and a portion of the depth measurements adjacent to the label position; generating an item search space in the common frame of reference according to the class of the label indicator; and determining, based on a subset of the depth measurements within the item search space, whether the item search space contains an item.