In recent years, visual SLAM has attracted wide attention due to its advantages of simple structure, low cost and ability to integrate semantic information. Loop closure detection plays an important role on it. According to the loop information obtained, the visual SLAM back-end optimization algorithm can optimize the pose according to the loop constraint, eliminate the cumulative error generated after long-term work, and achieve accurate long-term positioning, in order to build a globally consistent motion track and map. First introduce the principle and function of loop closure detection in visual SLAM, then conduct an in-depth analysis of the traditional bag-of-words model from feature extraction, similarity judgment, and experimental evaluation, and outlines several improved algorithms based on the bag-of-words model and probability, and summarizes several loop closure detection methods based on deep learning, briefly summarize the loop closure detection methods combined with semantic information, and finally summarize and prospect the current problems and future development of loop closure detection.