The localization of objects is a crucial task in various applications such as
robotics, virtual and augmented reality, and the transportation of goods in
warehouses. Recent advances in deep learning have enabled the localization
using monocular visual cameras. While structure from motion (SfM) predicts the
absolute pose from a point cloud, absolute pose regression (APR) methods learn
a semantic understanding of the environment through neural networks. However,
both fields face challenges caused by the environment such as motion blur,
lighting changes, repetitive patterns, and feature-less structures. This study
aims to address these challenges by incorporating additional information and
regularizing the absolute pose using relative pose regression (RPR) methods.
The optical flow between consecutive images is computed using the Lucas-Kanade
algorithm, and the relative pose is predicted using an auxiliary small
recurrent convolutional network. The fusion of absolute and relative poses is a
complex task due to the mismatch between the global and local coordinate
systems. State-of-the-art methods fusing absolute and relative poses use pose
graph optimization (PGO) to regularize the absolute pose predictions using
relative poses. In this work, we propose recurrent fusion networks to optimally
align absolute and relative pose predictions to improve the absolute pose
prediction. We evaluate eight different recurrent units and construct a
simulation environment to pre-train the APR and RPR networks for better
generalized training. Additionally, we record a large database of different
scenarios in a challenging large-scale indoor environment that mimics a
warehouse with transportation robots. We conduct hyperparameter searches and
experiments to show the effectiveness of our recurrent fusion method compared
to PGO