Monocular Camera Localization in Known Environments: An In-Depth Review

Monocular camera localization in known environments is a critical task for applications like autonomous navigation, augmented reality, and robotic positioning, requiring precise spatial awareness. Unlike localization in unknown environments, which builds maps in real time, this leverages pre-existing data for higher accuracy. This review comprehensively analyzes monocular camera localization methods in known environments, categorizing them into 2D-2D feature matching, 2D-3D feature matching, and regression-based approaches. It consolidates foundational techniques and recent advancements, providing inter-class and intra-class performance comparisons on mainstream datasets. Key findings show that 2D-3D methods generally offer the highest accuracy, especially in structured outdoor environments, due to robust use of 3D spatial information. However, recent scene coordinate regression methods, such as ACE and ACE++, achieve comparable or superior performance in indoor scenes with more efficient pipelines. This review highlights challenges and proposes future directions: (1) synthetic data generation to meet deep learning demands, while addressing domain gaps; (2) improving generalization to unseen scenes and reducing retraining; (3) multi-sensor fusion for enhanced robustness; (4) exploring transformer-based and graph neural network architectures; (5) developing lightweight models for real-time performance on resource-constrained devices. This review aims to guide researchers and practitioners in method selection and identify key research directions.