An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.
Smartphone-based pedestrian inertial tracking: Dataset, model, and deployment
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Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.