Shelf Slot Empty Detection Using Back-of-Shelf Plane Imaging
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Solution Overview
Problem
Current inventory management systems in retail environments face challenges in accurately detecting stock levels without the need for optical fiducials or prior knowledge of slot locations, leading to inefficiencies in restocking and potential stock discrepancies due to lighting conditions and varying perspectives.
Innovation Solution
A method utilizing a robotic system to autonomously navigate and capture both photographic and depth images of inventory structures, which calculates and normalizes the back-of-shelf plane to identify empty slots without the need for optical markers or prior knowledge of slot dimensions, by processing 2D and 3D data to determine stock conditions and generate restocking prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical fiducials or prior knowledge of slot locations are used for inventory detection, then detection accuracy is improved, but device complexity and infrastructure modification requirements increase
Solution Approach 1:
The system uses the existing shelf structure itself to provide detection references. By detecting the back-of-shelf plane from the shelf's own geometric features rather than requiring external fiducials, the shelf structure serves its dual purpose of both storage and detection reference, eliminating the need for additional infrastructure modifications
Solution Approach 2:
The patent extracts and utilizes the inherent geometric features of the shelf structure (the back-of-shelf plane) as the detection reference. Instead of adding external fiducials to the system, it isolates and leverages the existing shelf geometry that is already present in the inventory structure, removing the need for additional components
2Ease of manufacture
If traditional inventory detection methods are used, then implementation is simpler, but detection reliability deteriorates under varying lighting conditions and perspectives
Solution Approach 1:
The system transitions from 2D image analysis to 3D depth image analysis by detecting the back-of-shelf plane in three-dimensional space. This dimensional change allows the system to achieve perspective-invariant detection, as the 3D geometric structure of the shelf remains consistent regardless of viewing angle or lighting conditions
Solution Approach 2:
The patent changes the detection parameter from 2D pixel-based analysis to 3D spatial coordinate-based analysis. By working in three-dimensional space with depth information, the system achieves parameter invariance to lighting conditions and perspective changes, improving reliability while maintaining implementation feasibility
3Device complexity
If manual inventory checking is performed, then system complexity is reduced, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical inventory checking with an automated robotic system that uses depth imaging and computer vision algorithms. The robotic system autonomously navigates, captures 3D images, detects the back-of-shelf plane, and identifies empty slots, substituting human labor with automated mechanical and computational processes to improve productivity
Solution Approach 2:
The system enables self-service inventory monitoring by automatically detecting stock levels and generating restocking prompts without human intervention. The robotic system performs the entire inventory assessment process autonomously, from data collection to analysis to restocking notification, dramatically improving efficiency while keeping system complexity manageable
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes errors in stock detection, allows for real-time inventory tracking, and enables efficient restocking by accurately identifying empty slots across varying conditions, improving inventory management without modifying existing infrastructure.
Implementation Method 1
a depth camera to capture depth information of the inventory structure
Data Source
AI summary
A method for maintaining inventory within a store includes: accessing an image (e.g., a color image, depth image) depicting an inventory structure; detecting a slot region of the image depicting a slot; identifying a product type assigned to the slot; accessing a product dimension of the product type; defining a target region within the slot in the image based on the product dimension; defining a product region within the slot in the image based on the product dimension and the target region; defining a back-of-shelf plane intersecting the target region of the image; detecting a surface within the product region; and, in response to the surface intersecting the back-of-shelf plane, identifying the slot as empty and generating a prompt to restock the slot with product units of the product type.