Simultaneous Localisation and Mapping (SLAM) is one of the fundamental
problems in autonomous mobile robots where a robot needs to reconstruct a
previously unseen environment while simultaneously localising itself with
respect to the map. In particular, Visual-SLAM uses various sensors from the
mobile robot for collecting and sensing a representation of the map.
Traditionally, geometric model-based techniques were used to tackle the SLAM
problem, which tends to be error-prone under challenging environments. Recent
advancements in computer vision, such as deep learning techniques, have
provided a data-driven approach to tackle the Visual-SLAM problem. This review
summarises recent advancements in the Visual-SLAM domain using various
learning-based methods. We begin by providing a concise overview of the
geometric model-based approaches, followed by technical reviews on the current
paradigms in SLAM. Then, we present the various learning-based approaches to
collecting sensory inputs from mobile robots and performing scene
understanding. The current paradigms in deep-learning-based semantic
understanding are discussed and placed under the context of Visual-SLAM.
Finally, we discuss challenges and further opportunities in the direction of
learning-based approaches in Visual-SLAM.

本文回顾了 Visual-SLAM 领域中采用基于深度学习等基于数据驱动的方法来解决传统基于几何模型的技术在复杂环境中难以处理的 SLAM 问题的最新进展，并探讨了其面临的挑战和发展机遇。