A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

Abstract

Grocery shoppers must negotiate cluttered, crowded, and complex store layouts containing a vast variety of products to make their intended purchases. This complexity may prevent even experienced shoppers from finding their grocery items, consuming a lot of their time and resulting in monetary loss for the store. To address these issues, we present a generic grocery robot architecture for the autonomous search and localization of products in crowded dynamic unknown grocery store environments using a unique context Simultaneous Localization and Mapping (contextSLAM) method. The contextSLAM method uniquely creates contextually rich maps through the online fusion of optical character recognition and occupancy grid information to locate products and aid in robot localization in an environment. The novelty of our robot architecture is in its ability to intelligently use geometric and contextual information within the context map to direct robot exploration in order to localize products in unknown environments in the presence of dynamic people. Extensive experiments were conducted with a mobile robot to validate the overall architecture and contextSLAM, including in a real grocery store. The results of the experiments showed that our architecture was capable of searching for and localizing all products in various grocery lists in different unknown environments.

A Comprehensive Survey of Visual SLAM Algorithms

“…Simultaneous localization and mapping (SLAM) technology, first proposed by Smith in 1986 [1], is used in an extensive range of applications, especially in the domain of augmented reality (AR) [2][3][4] and robotics [5][6][7]. The SLAM process aims at mapping an unknown environment and simultaneously locating a sensor system in this environment through the signals provided by the sensor(s).…”