Navig-AI-tion: Navigation by Contextual AI and Spatial Audio

TL;DR

This paper introduces a navigation system combining a Vision Language Model with spatial audio cues to improve orientation and reduce errors in audio-only walking navigation.

Contribution

It presents a novel integration of environmental landmark extraction with directional spatial audio cues, enhancing audio-only navigation accuracy and user experience.

Findings

Spatial audio cues reduced route deviations in user study.

Users reported better orientation with landmark-anchored instructions.

The system outperformed VLM-only and Google Maps in navigation accuracy.

Abstract

Audio-only walking navigation can leave users disoriented, relying on vague cardinal directions and lacking real-time environmental context, leading to frequent errors. To address this, we present a novel system that integrates a Vision Language Model (VLM) with a spatial audio cue. Our system extracts environmental landmarks to anchor navigation instructions and, crucially, provides a directional spatial audio signal when the user faces the wrong direction, indicating the precise turn direction. In a user study (n=12), the spatial audio cue with VLM reduced route deviations compared to both VLM-only and Google Maps (audio-only) baseline systems. Users reported that the spatial audio cue effectively supported orientation and that landmark-anchored instructions provided a better navigation experience over audio-only Google Maps. This work serves as an initial look at the utility of future audio-only navigation systems for incorporating directional cues, especially real-time corrective spatial audio.