This paper provides a comprehensive survey of the evolution and advancements in Simultaneous Localization and Mapping (SLAM) techniques, focusing on the latest developments in radiance fields. The evolution of SLAM is traced from hand-crafted methods to deep learning-based approaches, with a significant shift in 2021 with the introduction of radiance-field-based approaches. The paper highlights the strengths and limitations of different SLAM methods, including NeRFs and 3D Gaussian Splatting (3DGS), and their impact on scene representation, tracking, mapping, and rendering. Key aspects of SLAM, such as global consistency, robust camera tracking, accurate surface modeling, real-time performance, and robustness to noisy data, are discussed. The paper reviews existing SLAM surveys, introduces key datasets and benchmarks, and describes evaluation metrics used in SLAM research. It also delves into the theoretical foundations of radiance fields, including NeRF and 3DGS, and their applications in SLAM. The paper categorizes and analyzes 80 SLAM systems published in the past three years, focusing on their performance in various scenarios. It highlights the rapid progress in SLAM, particularly in the areas of NeRF and 3DGS, and discusses the challenges and future research directions. The survey aims to serve as a valuable resource for both newcomers and experts in the field, providing insights into the latest advancements and their implications for the future of SLAM.This paper provides a comprehensive survey of the evolution and advancements in Simultaneous Localization and Mapping (SLAM) techniques, focusing on the latest developments in radiance fields. The evolution of SLAM is traced from hand-crafted methods to deep learning-based approaches, with a significant shift in 2021 with the introduction of radiance-field-based approaches. The paper highlights the strengths and limitations of different SLAM methods, including NeRFs and 3D Gaussian Splatting (3DGS), and their impact on scene representation, tracking, mapping, and rendering. Key aspects of SLAM, such as global consistency, robust camera tracking, accurate surface modeling, real-time performance, and robustness to noisy data, are discussed. The paper reviews existing SLAM surveys, introduces key datasets and benchmarks, and describes evaluation metrics used in SLAM research. It also delves into the theoretical foundations of radiance fields, including NeRF and 3DGS, and their applications in SLAM. The paper categorizes and analyzes 80 SLAM systems published in the past three years, focusing on their performance in various scenarios. It highlights the rapid progress in SLAM, particularly in the areas of NeRF and 3DGS, and discusses the challenges and future research directions. The survey aims to serve as a valuable resource for both newcomers and experts in the field, providing insights into the latest advancements and their implications for the future of SLAM.