Grid2Guide: A* Enabled Small Language Model for Indoor Navigation

TL;DR

Grid2Guide integrates A* search with a Small Language Model to provide accurate, human-readable indoor navigation instructions without relying on external signals or infrastructure.

Contribution

This paper introduces a hybrid framework combining A* search and a Small Language Model for real-time, infrastructure-free indoor navigation guidance.

Findings

Effective in various indoor scenarios

Produces accurate and timely navigation instructions

Lightweight and infrastructure-free solution

Abstract

Reliable indoor navigation remains a significant challenge in complex environments, particularly where external positioning signals and dedicated infrastructures are unavailable. This research presents Grid2Guide, a hybrid navigation framework that combines the A* search algorithm with a Small Language Model (SLM) to generate clear, human-readable route instructions. The framework first conducts a binary occupancy matrix from a given indoor map. Using this matrix, the A* algorithm computes the optimal path between origin and destination, producing concise textual navigation steps. These steps are then transformed into natural language instructions by the SLM, enhancing interpretability for end users. Experimental evaluations across various indoor scenarios demonstrate the method's effectiveness in producing accurate and timely navigation guidance. The results validate the proposed approach as a lightweight, infrastructure-free solution for real-time indoor navigation support.