A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
复制标题
DOI：
--
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Changhao Chen;B. Wang;Chris Xiaoxuan Lu;A. Trigoni;A. Markham
中科院分区：
文献类型：
--
作者：
Changhao Chen;B. Wang;Chris Xiaoxuan Lu;A. Trigoni;A. Markham
基于深度学习的定位和地图最近引起了人们的极大关注。与利用物理模型或几何理论创建手工设计的算法不同，基于深度学习的解决方案提供了一种以数据驱动的方式解决问题的替代方案。受益于不断增长的数据量和计算能力，这些方法正在迅速发展成为一个新的领域，提供准确和强大的系统来跟踪运动和估计场景及其结构，用于现实世界的应用。在这项工作中，我们提供了一个全面的调查，并提出了一个新的分类定位和地图使用深度学习。我们还讨论了当前模型的局限性，并指出了可能的未来方向。广泛的主题涵盖，从学习里程估计，映射，到全球定位和同步定位和映射（SLAM）。我们重新审视了车载传感器感知自我运动和场景理解的问题，并展示了如何通过将这些模块集成到未来的空间机器智能系统（SMIS）中来解决这个问题。我们希望这项工作能够连接机器人、计算机视觉和机器学习社区的新兴工作，并作为未来研究人员应用深度学习解决定位和地图问题的指南。
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.