Deep Learning for Visual Localization and Mapping: A Survey
Changhao Chen Bing Wang Chris Xiaoxuan Lu Niki Trigoni Andrew Markham
Deep learning based localization and mapping approaches have recently emerged
as a new research direction and receive significant attentions from both
industry and academia. Instead of creating hand-designed algorithms based on
physical models or geometric theories, deep learning solutions provide an
alternative to solve the problem in a data-driven way. Benefiting from the
ever-increasing volumes of data and computational power on devices, these
learning methods are fast evolving into a new area that shows potentials to
track self-motion and estimate environmental model accurately and robustly for
mobile agents. In this work, we provide a comprehensive survey, and propose a
taxonomy for the localization and mapping methods using deep learning. This
survey aims to discuss two basic questions: whether deep learning is promising
to localization and mapping; how deep learning should be applied to solve this
problem. To this end, a series of localization and mapping topics are
investigated, from the learning based visual odometry, global relocalization,
to mapping, and simultaneous localization and mapping (SLAM). It is our hope
that this survey organically weaves together the recent works in this vein from
robotics, computer vision and machine learning communities, and serves as a
guideline for future researchers to apply deep learning to tackle the problem
of visual localization and mapping.