Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
arXiv:2605.07412v2 Announce Type: replace Abstract: 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
The proliferation of shared mobility and the increasing complexity of urban environments necessitate more robust and cost-effective tracking solutions beyond traditional GNSS, which is often blocked.
Improved bike tracking in difficult environments can enhance urban logistics, asset management, and potentially open new avenues for autonomous last-mile delivery systems using similar navigation principles.
The development of reliable, low-cost inertial navigation for shared assets could reduce operational costs and expand the viable deployment areas for large-scale urban mobility services.
- · Shared mobility companies
- · Urban logistics providers
- · Inertial sensor manufacturers
- · Smart city developers
- · Companies reliant solely on GNSS solutions for tracking
- · High-cost sensor manufacturers (e.g., LiDAR, high-end visual systems for asset t
More accurate and reliable tracking of shared bikes and other assets in GNSS-denied urban areas becomes possible.
Reduced operational costs for shared mobility services due to improved asset management and theft prevention, potentially leading to wider adoption.
The technology could be adapted for other low-cost autonomous urban applications, fostering innovation in last-mile delivery or mobile sensor networks.
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