Localization Uncertainty

Localization uncertainty, the inherent imprecision in determining an object's or robot's position, is a critical challenge across numerous fields, with research focusing on minimizing this uncertainty to improve the reliability of autonomous systems and robotic applications. Current approaches leverage various techniques, including probabilistic models (e.g., Gaussian processes, Kalman filters), information-driven path planning, and uncertainty-aware neural networks (e.g., NeRFs) to estimate and mitigate localization errors. Addressing this uncertainty is crucial for enhancing the safety and performance of autonomous vehicles, multi-robot systems, and other applications relying on precise position information in challenging environments, such as underwater or GPS-denied settings.