Loop closure detection is an indispensable part of visual simultaneous location and mapping (SLAM). Correct detection of loop closure can help mobile robot to reduce the problem of cumulative pose drift. At present, the main method for detecting visual SLAM loop closure is the bag of words (BoW) model, but it lacks the spatial distribution information of local features of the image, and the scale will become larger and larger with the increase of data, resulting in the slow operation speed. In order to solve these problems, the image histogram and the key region covariance matrix matching method are used to visually detect the loop closure combined with the global and local image features. In this paper, three different place recognition techniques are studied: histogram only, image covariance matrix matching (ICMM) and cluster loop. Experiments on real datasets show that the proposed method of detecting the loop closure is better than the traditional methods in detecting accuracy and recalling rate, which also improves the operation effect of the SLAM algorithm.

闭环检测是视觉同步定位与建图（SLAM）中不可或缺的一部分。正确检测闭环可以帮助移动机器人减少累积位姿漂移问题。目前检测视觉SLAM闭环的主要方法是词袋（BoW）模型，但它缺乏图像局部特征的空间分布信息，并且随着数据的增加尺度会越来越大，导致运行速度缓慢。为了解决这些问题，利用图像直方图和关键区域协方差矩阵匹配方法，结合全局和局部图像特征，对回环进行视觉检测。在本文中，研究了三种不同的地点识别技术：仅直方图、图像协方差矩阵匹配 (ICMM) 和聚类循环。