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Semi-supervised Vector-Quantization in Visual SLAM using HGCN. (arXiv:2207.06738v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
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 …
arxiv cv quantization semi-supervised slam vector visual slam