Robot localization: a comprehensive survey from classical methods to intelligent autonomy

Building on previous surveys that often focus on specific components of robot localization, this work offers an integrated review spanning algorithmic estimators, mapping paradigms, and sensor modalities. The survey proposes a functional taxonomy that links probabilistic inference, map representations, and sensor-derived spatial information within a unified comparative framework. By examining classical approaches alongside emerging trends such as semantic mapping and learning-augmented estimators, the paper outlines performance trade-offs, open challenges, and potential directions for future research. Structured tables provide a comparative overview of each class of methods across accuracy, robustness, scalability, and computational cost.