Fast and robust loop-closure detection using deep neural networks and matrix transformation for a visual SLAM system

We investigate the loop-closure detection problem for a visual simultaneous localization and mapping (SLAM) system. Generally, a deep neural network is used to solve loop-closure detection problems. However, this method is time-consuming and occupies a large memory in practice, primarily owing to the high-dimensional nature of the data involved. Herein, we combine a pretrained convolutional neural network (CNN) model with a convolutional autoencoder to acquire a low-dimensional CNN vector as an image representation. Subsequently, the loop-closure problem is simulated by the single-image nearest-neighbor search method based on the cosine distance of the low-dimensional CNN vectors. In addition, to solve the perceptual aliasing problem in loop-closure detection, we empirically propose an efficient method for eliminating false positive loop closures by appropriately decomposing and reconstructing the similarity matrix. Furthermore, we verify through experiments that our method can effectively reduce the time and memory costs of loop-closure detection tasks while ensuring detection accuracy. Therefore, this method can provide a feasible solution to improve the real-time performance and robustness of visual SLAM systems.