Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning

This paper describes a novel system for automating data collection and surveying in a retail store using mobile robots. The manpower cost for surveying and monitoring the shelves in retail stores are high, because of which these activities are not repeated frequently causing reduced customer satisfaction and loss of revenue. Further, the accuracy of data collected may be improved by avoiding human related factors. We use a mobile robot platform with on-board cameras to monitor the shelves autonomously (based on indoor UWB Localization and planning). The robot is designed to facilitate automatic detection of Shelf Out of Stock (SOOS) situations. The paper contribution is an approach to estimate the overall stock assortment based of pictures from both visual and textual clues. Based on visual and textual features extracted from two trained Convolutional Neural Networks (CNNs), the type of the product is identified by a machine learning classifier. The approach was applied and tested on a newly collected dataset and several machine learning algorithms are compared. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach, also in comparison of existing state of the art SOOS solutions.