  In this paper, we introduce AE-FABMAP, a new self-supervised bag of
words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the
current state of the art BoW-based path planning algorithm. That is, we have
used a deep convolutional autoencoder to find loop closures. In the context of
bag of words visual SLAM, vector quantization (VQ) is considered as the most
time-consuming part of the SLAM procedure, which is usually performed in the
offline phase of the SLAM algorithm using unsupervised algorithms such as
Kmeans++. We have addressed the loop closure detection part of the BoW-based
SLAM methods in a self-supervised manner, by integrating an autoencoder for
doing vector quantization. This approach can increase the accuracy of
large-scale SLAM, where plenty of unlabeled data is available. The main
advantage of using a self-supervised is that it can help reducing the amount of
labeling. Furthermore, experiments show that autoencoders are far more
efficient than semi-supervised methods like graph convolutional neural
networks, in terms of speed and memory consumption. We integrated this method
into the state of the art long range appearance based visual bag of word SLAM,
FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this
approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and
it achieves higher accuracy in loop closure detection and trajectory
generation.
