Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors
arXiv:2607.05663v1 Announce Type: cross Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments. While fusing Inertial Measurement Unit (IMU) data with satellite-based correction signals provides precise vehicle pose estimates, performance degrades substantially during outages. Recent studies indicate that Machine Learning (ML) can improve IMU-based proprioceptive localization, highlighting untapped potential for onboard sensors readily available in production vehicles. This paper introduces Physics-Regularized Machine Learning for Loc
The increasing demand for robust autonomous mobility systems and the limitations of satellite-based localization during outages are driving innovation in proprioceptive sensing.
Improving the accuracy and robustness of vehicle localization using readily available onboard sensors reduces dependency on external signals and enhances the safety and reliability of autonomous vehicles.
Machine Learning is being integrated with physics-based models to enhance the performance of IMU-based localization, creating more resilient and adaptable autonomous systems.
- · Autonomous vehicle manufacturers
- · ML/AI developers
- · Sensor manufacturers
- · Companies reliant solely on satellite navigation systems
Autonomous vehicles gain more reliable and independent navigation capabilities.
Reduced infrastructure costs for localization as systems rely more on onboard sensors.
Accelerated deployment of autonomous fleets in diverse and challenging environments without consistent external signal coverage.
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Read at arXiv cs.AI