Loop Closure Detection for Monocular Visual Odometry: Deep-Learning Approaches Comparison

Abstract

In order to decrease monocular visual odometry drift by detecting loop closure, this paper presents a comparison between state of the art, 2-channel and Siamese, Convolutional Neural Networks. The work consists of training these networks in order to make them able to robustly identify loop closures. As we are in the case of having two input images, we perform our trainings and tests on both 2-channel and Siamese architecture for each network.