Loop-closure detection plays an important role in visual simultaneous localisation and mapping;it is an independent part of the visual odometer and can effectively reduce its accumulated error, in addition to helping with loop-closure detection for relocalisation. With the development of deep learning methods in recent years, the training models of convolutional neural networks for major data sets have been improved for loop-closure detection. Presently, some high-level engineering problems still rely on auxiliary equipment, such as panoramic cameras and radar lasers, which greatly increase the expensive extra cost; however, owing to the extreme appearance and viewpoint changes involved in such problems, loop-closure detection that relies on two-dimensional images is not applicable. Based on the two nearest neighbour vector of locally aggregated descriptors (TNNVLAD) method, a novel feature descriptor called two nearest neighbour local sensor tensor(TNNLoST) is proposed herein by combining the semantic features of high-level neural networks with dense descriptors. This approach introduces a semantic concept similar to human cognition for the surrounding environment, thus enabling better understanding of the environment. The proposed method was applied to publicly available benchmark datasets to show its performance.

闭环检测在视觉同步定位和建图中起着重要作用，它是视觉里程计的一个独立部分，除了有助于闭环检测进行重定位外，还可以有效减少其累积误差。近年来随着深度学习方法的发展，针对主要数据集的卷积神经网络的训练模型得到了改进，用于闭环检测。目前，一些高水平的工程问题仍然依赖于辅助设备，如全景相机和雷达激光器，大大增加了昂贵的额外成本；然而，由于此类问题涉及的极端外观和视点变化，依赖于二维图像的闭环检测不适用。基于局部聚合描述符的两个最近邻向量（TNNVLAD）方法，本文通过将高级神经网络的语义特征与密集描述符相结合，提出了一种称为两个最近邻局部传感器张量（TNNLoST）的新特征描述符。这种方法引入了类似于人类对周围环境认知的语义概念，从而能够更好地理解环境。将所提出的方法应用于公开可用的基准数据集以显示其性能。这种方法引入了类似于人类对周围环境认知的语义概念，从而能够更好地理解环境。将所提出的方法应用于公开可用的基准数据集以显示其性能。这种方法引入了类似于人类对周围环境认知的语义概念，从而能够更好地理解环境。将所提出的方法应用于公开可用的基准数据集以显示其性能。