Accurate Pedestrian Tracking in Urban Canyons: A Multi-Modal Fusion Approach

TL;DR

This paper presents a multi-modal fusion approach combining GNSS, inertial data, and map priors to improve pedestrian localization accuracy in urban environments, especially benefiting visually impaired users.

Contribution

It introduces a particle filter-based fusion method that integrates GNSS, inertial data via RoNIN, and spatial priors for enhanced urban pedestrian tracking.

Findings

Fused approach outperforms GNSS alone on most metrics.

Inertial-only localization with particle filtering surpasses GNSS in critical measures.

System effectively improves sidewalk and street crossing accuracy.

Abstract

The contribution describes a pedestrian navigation approach designed to improve localization accuracy in urban environments where GNSS performance is degraded, a problem that is especially critical for blind or low-vision users who depend on precise guidance such as identifying the correct side of a street. To address GNSS limitations and the impracticality of camera-based visual positioning, the work proposes a particle filter based fusion of GNSS and inertial data that incorporates spatial priors from maps, such as impassable buildings and unlikely walking areas, functioning as a probabilistic form of map matching. Inertial localization is provided by the RoNIN machine learning method, and fusion with GNSS is achieved by weighting particles based on their consistency with GNSS estimates and uncertainty. The system was evaluated on six challenging walking routes in downtown San Francisco using three metrics related to sidewalk correctness and localization error. Results show that the fused approach (GNSS+RoNIN+PF) significantly outperforms GNSS only localization on most metrics, while inertial-only localization with particle filtering also surpasses GNSS alone for critical measures such as sidewalk assignment and across street error.