  In this paper, two semi-supervised appearance based loop closure detection
technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to
the current state of the art localization SLAM algorithm, ORB-SLAM, is
presented. The proposed HGCN-FABMAP method is implemented in an off-line manner
incorporating Bayesian probabilistic schema for loop detection decision making.
Specifically, we let a Hyperbolic Graph Convolutional Neural Network (HGCN) to
operate over the SURF features graph space, and perform vector quantization
part of the SLAM procedure. This part previously was performed in an
unsupervised manner using algorithms like HKmeans, kmeans++,..etc. The main
Advantage of using HGCN, is that it scales linearly in number of graph edges.
Experimental results shows that HGCN-FABMAP algorithm needs far more cluster
centroids than HGCN-ORB, otherwise it fails to detect loop closures. Therefore
we consider HGCN-ORB to be more efficient in terms of memory consumption, also
we conclude the superiority of HGCN-BoW and HGCN-FABMAP with respect to other
algorithms.
