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.

翻译：本文提出了一种行人导航方法，旨在提升GNSS性能下降的城市环境中的定位精度。该问题对于依赖精确引导（如识别街道正确侧）的视障或低视力用户尤为关键。为应对GNSS的局限性以及基于摄像头的视觉定位在实际应用中的困难，本研究提出了一种基于粒子滤波的GNSS与惯性数据融合方法，该方法融合了来自地图的空间先验信息（如不可通行的建筑物与不可能行走区域），起到一种概率形式的地图匹配作用。惯性定位由RoNIN机器学习方法提供，通过与GNSS的融合实现——依据粒子与GNSS估计值及不确定度的一致性进行加权。该系统在旧金山市中心的六条具有挑战性的步行路线上进行了评估，使用了与行人道正确性和定位误差相关的三项指标。结果表明，在多数指标上，融合方法（GNSS+RoNIN+PF）显著优于仅使用GNSS的定位；同时，仅使用惯性数据并辅以粒子滤波的定位方法，在行人道归属判断和跨街道误差等关键指标上也优于单一的GNSS定位。