A Survey on Deep Learning for Localization and Mapping: Towards the Age
of Spatial Machine Intelligence
- URL: http://arxiv.org/abs/2006.12567v2
- Date: Mon, 29 Jun 2020 18:58:07 GMT
- Title: A Survey on Deep Learning for Localization and Mapping: Towards the Age
of Spatial Machine Intelligence
- Authors: Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, Andrew
Markham
- Abstract summary: We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning.
A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping.
It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
- Score: 48.67755344239951
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep learning based localization and mapping has recently attracted
significant attention. Instead of creating hand-designed algorithms through
exploitation of physical models or geometric theories, deep learning based
solutions provide an alternative to solve the problem in a data-driven way.
Benefiting from ever-increasing volumes of data and computational power, these
methods are fast evolving into a new area that offers accurate and robust
systems to track motion and estimate scenes and their structure for real-world
applications. In this work, we provide a comprehensive survey, and propose a
new taxonomy for localization and mapping using deep learning. We also discuss
the limitations of current models, and indicate possible future directions. A
wide range of topics are covered, from learning odometry estimation, mapping,
to global localization and simultaneous localization and mapping (SLAM). We
revisit the problem of perceiving self-motion and scene understanding with
on-board sensors, and show how to solve it by integrating these modules into a
prospective spatial machine intelligence system (SMIS). It is our hope that
this work can connect emerging works from robotics, computer vision and machine
learning communities, and serve as a guide for future researchers to apply deep
learning to tackle localization and mapping problems.
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