Semantic SLAM: A comprehensive survey of methods and applications

This paper surveys the different approaches in semantic Simultaneous Localization and Mapping (SLAM), exploring how the incorporation of semantic information has enhanced performance in both indoor and outdoor settings, while highlighting key advancements in the field. It also identifies existing gaps and proposes potential directions for future improvements to address these issues. We provide a detailed review of the fundamentals of semantic SLAM, illustrating how incorporating semantic data enhances scene understanding and mapping accuracy. The paper presents semantic SLAM methods and core techniques that contribute to improved robustness and precision in mapping. A comprehensive overview of commonly used datasets for evaluating semantic SLAM systems is provided, along with a discussion of performance metrics used to assess their efficiency and accuracy. To demonstrate the reliability of semantic SLAM methodologies, we reproduce selected results from existing studies offering insights into the reproducibility of these approaches. The paper also addresses key challenges such as real-time processing, dynamic scene adaptation, and scalability while highlighting future research directions. Unlike prior surveys, this paper uniquely combines (i) a systematic taxonomy of semantic SLAM approaches across different sensing modalities and environments, (ii) a comparative review of datasets and evaluation metrics, and (iii) a reproducibility study of selected methods. To our knowledge, this is the first survey that integrates methods, datasets, evaluation practices, and application insights into a single comprehensive review, thereby offering a unified reference for researchers and practitioners. In conclusion, this review underscores the vital role of semantic SLAM in driving advancements in autonomous systems and intelligent navigation by analyzing recent developments, validating findings, and highlighting future research directions.