State Estimation Drift Mitigation

State estimation drift, the accumulation of errors over time in systems estimating their position or state, is a significant challenge across various fields, from robotics and autonomous vehicles to sensor networks and machine learning. Current research focuses on mitigating this drift using diverse approaches, including advanced filtering techniques (like Kalman filters and extended Kalman smoothers), data-driven methods such as neural networks and transformers that learn to correct for errors, and incorporating richer sensor data or higher-level features (planes, magnetic fields) to improve robustness. Successfully addressing state estimation drift is crucial for improving the reliability and accuracy of numerous applications, enabling more robust autonomous systems, improved sensor data analysis, and more accurate machine learning models in dynamic environments.