Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
For large-scale shared bike operators, this provides a practical, low-cost solution for accurate tracking in urban canyons where GNSS fails.
The paper tackles bike tracking in GNSS-blocked environments using only low-cost inertial sensors. Their proposed inertial tracking framework, integrating bicycle mechanical constraints with a mixture-of-experts model, improves localization accuracy by at least 12% over baselines and achieves wheel speed errors below 0.5 m/s at 95-percentile.
Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons. Despite decades of research, localization using only low-cost inertial sensors still faces challenges such as cumulative drifts and poor robustness caused by filtering methods. Furthermore, sensors such as visual and LiDAR could provide reliable measurements, but they are not suitable for large-scale deployment. In this paper, we propose an inertial tracking framework that integrates bicycle mechanical constraints with a mixture-of-experts model. Specifically, we leverage multiple expert modules to capture shared representations and weight them through the gating mechanism, thus improving multi-task learning performance and enabling uncertainty-aware trajectory estimation. Furthermore, based on the mechanical transmission between the pedal and the rear wheel of a bike, we explore the intrinsic relationship between the rider's periodic pedalling behaviors and acceleration variations, and convert such patterns into bike's wheel speed for dynamic calibration. Experiments with real-world riding data from shared bikes of the DiDi ride-hailing platform demonstrate that our system improves the accuracy of baselines by at least 12%, with wheel speed errors below 0.5 m/s at 95-percentile.