Smart Technology and Artificial Intelligence
An AI-Driven Context-Aware Framework for Indoor Item Discovery and Shelf-Level Navigation in Large-Scale Retail Stores
Authors: Suresh Kumar
Publishing Date: 13-10-2026
ISBN: 978-81-975670-2-5
Abstract
Finding a specific item in a large retail store requires more than navigating to an aisle. A useful assistance system must interpret a shopper’s request, identify the intended product variant, determine where that product is currently displayed, guide the shopper to a suitable viewing position, and verify the item on a crowded shelf. These requirements connect indoor positioning, visual recognition, catalog retrieval, and human interaction, yet their errors arise at different spatial and semantic scales. This paper proposes a context-aware framework that coordinates these functions through a versioned semantic store graph and an explicit evidence state. The framework separates global wayfinding from local shelf inspection and withholds exact item confirmation when location, identity, or freshness evidence is insufficient. It incorporates accessibility constraints, uncertain inventory, changing planograms, and optional visual, inertial, and radio measurements. The research contribution is a system design and analytical evaluation, rather than a claim of deployed retail performance. Reproducible calculations examine target width, localization uncertainty, mapfreshness, correlated sensing, and disruption-aware routing. An executed Monte Carlo check uses 100,000 synthetic samples at each of five assumed error levels to verify an idealized interval-assignment model. For a 0.60 m target interval, the analytical assignment probability decreases from 86.64% at a 0.20 m error standard deviation to 29.23% at 0.80 m. These values describe the stated model, not a trained system or a store trial. The analysis motivates separate confidence requirements for aisle guidance and shelf confirmation. A prospective evaluation protocol defines end-to-end success, uncertainty calibration, generalization, accessibility, and operational maintenance measures. The proposed framework provides a testable basis for retail navigation research while identifying the empirical evidence needed before operational claims can be made.
Keywords
indoor navigation; retail item discovery; context awareness; shelf localization; multimodal perception; uncertainty; semantic maps; assistive shopping.
Cite as
Suresh Kumar, "An AI-Driven Context-Aware Framework for Indoor Item Discovery and Shelf-Level Navigation in Large-Scale Retail Stores", In: Mukesh Saraswat, Sandeep Kumar, Manjunatha Sughaturu Krishnappa and Rakesh Keshava (eds), Smart Technology and Artificial Intelligence, SCRS, India, 2026, pp. 74-103. https://doi.org/10.56155/978-81-975670-2-5-7