# Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments

arXiv: https://arxiv.org/abs/2605.07412 · HTML: https://mlsanity.com/paper/2605.07412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 17 of 17)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)
- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Neural Inertial Localization](https://mlsanity.com/paper/2203.15851/) (2022)
- [CTIN: Robust Contextual Transformer Network for Inertial Navigation](https://mlsanity.com/paper/2112.02143/) (2021)
- [Learned Inertial Odometry for Autonomous Drone Racing](https://mlsanity.com/paper/2210.15287/) (2022)
- [EqNIO: Subequivariant Neural Inertial Odometry](https://mlsanity.com/paper/2408.06321/) (2024)
- [Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones](https://mlsanity.com/paper/1808.03485/) (2018)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023)
- [MetroLoc: Metro Vehicle Mapping and Localization with LiDAR-Camera-Inertial Integration](https://mlsanity.com/paper/2111.00762/) (2021)
- [Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement](https://mlsanity.com/paper/2504.00445/) (2025)

## Abstract (arXiv)

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.