# A Vision-Assisted Acoustic Channel Modeling Framework for Smartphone Indoor Localization

## TL;DR

This paper introduces a smartphone-based indoor localization method that combines vision and acoustics to improve accuracy and robustness in complex environments.

## Contribution

A novel vision-assisted acoustic channel modeling framework that integrates visual and acoustic data for more accurate indoor localization.

## Key findings

- The method achieves mean localization errors of 0.096 m in static tests and 0.115 m in dynamic tests.
- The fusion of vision and acoustic data improves robustness against multipath reflections and occlusions.
- The approach provides calibratable variances for TOA measurements, enhancing downstream localization performance.

## Abstract

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation.