Stacked Denoising Auto-encoder Based Image Representation for Visual Loop Closure Detection

Loop closure detection is important in S-LAM (Simultaneous Location and Mapping) for its capability of relocation. Many techniques have been proposed such as Kalman filtering based methods. On the other hand, loop closure in the visual based SLAM can also be treated as an image retrieval problem. In recently years, deep learning is paid great attention and it is very appropriate for image classification and retrieval. However, deep learning usually askes for big data which may not be satisfied in visual based SLAM. In this paper, we proposed an unsupervised image retrieval method for loop closure detection. The SDA (Stacked Auto-encoder) is employed to translate images to high-dimensional representations, and then loop clousre detection is manipulated. The experiments show that, our method outperform the traditional BoW(Bag-of-Word) method in the 'New College' dataset and 'City Centre' dataset.