School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia.
Sensors (Basel). 2022 Sep 25;22(19):7265. doi: 10.3390/s22197265.
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.
同时定位与建图（SLAM）是自主移动机器人的基本问题之一，机器人需要在对地图进行自身定位的同时，对先前未见过的环境进行重建。具体来说，视觉 SLAM 使用移动机器人的各种传感器来收集和感知地图的表示。传统上，基于几何模型的技术被用于解决 SLAM 问题，但在具有挑战性的环境下往往容易出错。计算机视觉领域的最新进展，如深度学习技术，为解决视觉 SLAM 问题提供了一种数据驱动的方法。本综述总结了使用各种基于学习的方法在视觉 SLAM 领域的最新进展。我们首先简要概述了基于几何模型的方法，然后对 SLAM 的当前范例进行技术回顾。然后，我们介绍了从移动机器人收集传感器输入并执行场景理解的各种基于学习的方法。讨论了基于深度学习的语义理解的当前范例，并将其置于视觉 SLAM 的上下文中。最后，我们讨论了基于学习的视觉 SLAM 方法的挑战和进一步的机会。
