A Loop Closure Detection Method Based On CNN For Extracting Global And Local Features

Abstract

Loop closure detection (LCD) is an important module of visual SLAM (Simultaneous Localization and Mapping), which can be regarded as an image retrieval task. The correct LCD can effectively reduce the cumulative errors during visual SLAM localization and obtain a globally consistent map. The currently common approach to LCD is based on a bag-of-words (BoW) and traditional manually designed features. However, The BoW-based method ignores the spatial location information and semantic information of visual features, and image aliasing will affect the detection accuracy of the algorithm. Meanwhile, the artificially designed features are very sensitive to light changes. This paper proposes a LCD algorithm based on convolutional neural networks (CNN) to extract global and local features. Extensive experiments on six publicly available datasets have shown that the method provides high recall with 100% accuracy in most of the evaluated datasets relative to traditional algorithms and some deep learning-based methods.