Abstract: With the rapid advancement of autonomous driving and mobile robotics, loop closure detection in Visual Simultaneous Localization and Mapping (VSLAM) has emerged as a critical research focus due to its piv-otal role in eliminating cumulative errors and constructing globally consistent maps. This paper introduces the core value and research background of loop closure detection technology for visual SLAM, outlines the core principles of various loop closure detection methods based on traditional features, deep learning fea-tures, geometric topology, semantic information, and multi-modal fusion, reviews the application scenarios of three mainstream benchmark datasets (New College, TUM RGBD, and Oxford Robot-Car), and analyzes the quantitative performance of different methods in terms of precision, recall, and real-time performance (ms/frame) on CPUs with high and low performance. The analysis shows that multi-modal fusion and recent new methods are suitable for high-performance hardware and have better comprehensive performance, while traditional feature-based methods are more applicable to low-performance platforms; it points out the shortcomings of existing methods in dynamic scene adaptation and computational lightweighting, and fu-ture efforts need to promote the evolution of loop closure detection technology towards real-time, low-cost, and strong environmental adaptability.