Robot Shelf Imaging With Label Extraction for Low-Storage Inventory
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Solution Overview
Problem
Existing inventory monitoring systems in retail or warehouse environments are costly, time-consuming, and require substantial human intervention to track product availability, as they struggle to accurately identify misplaced, stolen, or damaged products, and do not account for real-time stock updates.
Innovation Solution
A movable camera system mounted on an autonomous robot that constructs a realogram, an updateable map of product positions, using multiple cameras and onboard processing to detect shelf labels, define product bounding boxes, and associate them with shelf labels, enabling automated inventory tracking and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture high resolution images of all products for accurate inventory monitoring, then measurement precision is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the essential information (shelf labels and product bounding boxes) from the captured images, discarding the rest of the image data. This extraction approach maintains accurate product identification while dramatically reducing the quantity of data that needs to be stored and processed.
Solution Approach 2:
The system creates simplified representations (copies) of the product information through bounding boxes and shelf label text, rather than storing the original high-resolution images. These copies contain the necessary identification data in a compact format that requires minimal storage space.
2Measurement precision
If manual monitoring methods are used to track product inventory and position, then measurement precision can be maintained through human judgment, but productivity decreases and time consumption increases
Solution Approach 1:
The system performs self-service by automatically capturing images, identifying shelf labels, defining product bounding boxes, and tracking inventory positions without human intervention. The robotic device independently navigates the warehouse, processes images using onboard computing, and updates inventory records autonomously.
Solution Approach 2:
The system replaces manual mechanical monitoring methods with an automated robotic system equipped with cameras and image processing algorithms. This substitution eliminates human labor while maintaining or improving measurement precision through consistent automated detection of product positions and inventory status.
3Productivity
If fixed position cameras are deployed throughout the store to monitor aisles, then productivity improves through continuous monitoring, but device complexity and installation cost increase
Solution Approach 1:
The system transitions from static fixed-position cameras to a dynamic mobile robotic platform that moves through the warehouse. This dynamic approach allows a single device to cover multiple locations sequentially, providing continuous monitoring capability without requiring complex installations at numerous fixed positions throughout the facility.
Data Source
AI summary
A method for greatly reducing data storage requirements of autonomous robots capable of product inventory is described. Instead using high resolution images for label identification and position tracking, a lower resolution map can be searched. Human or machines can identify position of labels, and via a reverse mapping, the corresponding position of a label on the high resolution image can be identified. Except for those portions of the high resolution image showing a label, most of the high resolution image can be discarded. Further processing on the limited image subset can be used to read the labels.