Deep Learning for Visual Localization and Mapping: A Survey
Deep Learning for Visual Localization and Mapping: A Survey
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DOI：
10.48550/arxiv.2308.14039
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发表时间：
2023-08
影响因子：
10.4
通讯作者：
Changhao Chen;Bing Wang;Chris Xiaoxuan Lu;Niki Trigoni;A. Markham
中科院分区：
文献类型：
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作者：
Changhao Chen;Bing Wang;Chris Xiaoxuan Lu;Niki Trigoni;A. Markham
基于深度学习的定位和映射方法最近成为一个新的研究方向，并受到工业界和学术界的高度关注。深度学习解决方案不是基于物理模型或几何理论创建手工设计的算法，而是提供了一种以数据驱动的方式解决问题的替代方案。受益于设备上不断增加的数据量和计算能力，这些学习方法正在快速发展成为一个新的领域，该领域显示出跟踪自我运动并为移动的代理准确和鲁棒地估计环境模型的潜力。在这项工作中，我们提供了一个全面的调查，并提出了使用深度学习的本地化和映射方法的分类。本调查旨在讨论两个基本问题：深度学习是否有希望用于本地化和映射，以及如何应用深度学习来解决这个问题。为此，一系列的定位和映射的主题进行了研究，从基于学习的视觉测距和全球重定位到映射，并同时定位和映射（SLAM）。我们希望这项调查有机地将机器人、计算机视觉和机器学习社区最近在这方面的工作编织在一起，并为未来的研究人员应用深度学习来解决视觉定位和映射问题提供指导。
Deep-learning-based localization and mapping approaches have recently emerged as a new research direction and receive significant attention 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 potential to track self-motion and estimate environmental models 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 for localization and mapping, and 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 and 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.