Technology Category
Intelligent Robots: Localization, Mapping, and NavigationPlanning, Routing, and Scheduling: Activity and Plan RecognitionComputer Vision: Low Level & Physics-based Vision
Accurate Pedestrian Tracking in Urban Canyons: A Multi-Modal Fusion Approach
🤖 AI Summary
This work addresses the challenge of accurate street-level localization for pedestrians—particularly blind or low-vision users—in urban canyons where GNSS signals are severely degraded. The authors propose a multimodal positioning approach that fuses GNSS, inertial navigation, and map-based spatial priors within a particle filter framework. High-precision inertial trajectories are obtained using RoNIN, while map-derived impassable regions serve as hard constraints. Crucially, semantic map priors are incorporated probabilistically into particle weight updates, and the contribution of GNSS observations is dynamically modulated based on their consistency. Evaluated on six complex routes in San Francisco, the method significantly outperforms GNSS-only and inertial-plus-particle-filter baselines in sidewalk-side classification accuracy and cross-street error, maintaining reliable street-level localization even during complete GNSS outages.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Pedestrian Tracking
Urban Canyons
GNSS Degradation
Localization Accuracy
Blind Navigation
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-modal fusion
particle filter
map matching
RoNIN
urban canyon localization
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