融合惯性与地图先验，提升城市峡谷中行人定位精度。
Accurate Pedestrian Tracking in Urban Canyons: A Multi-Modal Fusion Approach
- 用粒子滤波融合GNSS与惯性数据，引入地图约束提高定位可信度。
- 在旧金山六条路线测试中，融合方案在多数指标上优于纯GNSS。
- 适合盲人或低视力者导航，尤其改善过街与人行道识别准确率。
针对城市峡谷中GNSS信号衰减导致的定位难题，本文提出一种基于粒子滤波的多模态融合方法，结合GNSS、惯性数据与地图空间先验（如不可通行建筑和非步行区域），实现概率化地图匹配。惯性定位采用RoNIN机器学习方法，通过粒子权重反映其与GNSS估计的一致性及不确定性。系统在旧金山市中心六条复杂步行路径上评估，使用与人行道正确性和定位误差相关的三项指标。结果表明，融合方案（GNSS+RoNIN+PF）在多数指标上显著优于仅用GNSS；而仅用惯性数据配合粒子滤波也优于纯GNSS，在关键指标如人行道分配和过街误差方面表现更优。
原文摘要 · Abstract (English)
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.
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