SuperVO: A Monocular Visual Odometry based on Learned Feature Matching with GNN

Abstract

In this paper, we propose a novel Visual Odometry (VO) system using a feature detector and feature matcher based on neural networks. The networks for feature detectors and descriptors learning consists of a conventional CNN for feature detection and description, and a graph neural network (GNN) final feature matching. The learned feature has several advantages over traditional handcrafted features such as being robust to light variation and scale. By applying state-of-the-art deep learning-based feature Matcher-SuperGlue, we developed a new monocular VO framework which can exploit the advantages of deep learning-based feature detector and matcher, which performs better than many other learning-based VO methods.