Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control. This article reviews recent progress in SLAM, focusing on advances in the expressive capacity of the environmental models used in SLAM systems (representation) and the performance of the algorithms used to estimate these models from data (inference). A prominent theme of recent SLAM research is the pursuit of environmental representations (including learned representations) that go beyond the classical attributes of geometry and appearance to model properties such as hierarchical organization, affordance, dynamics, and semantics; these advances equip autonomous agents with a more comprehensive understanding of the world, enabling more versatile and intelligent operation. A second major theme is a revitalized interest in the mathematical properties of the SLAM estimation problem itself (including its computational and information-theoretic performance limits); this work has led to the development of novel classes of certifiable and robust inference methods that dramatically improve the reliability of SLAM systems in real-world operation. We survey these advances with an emphasis on their ramifications for achieving robust, long-duration autonomy, and conclude with a discussion of open challenges and a perspective on future research directions.
翻译:同时的本地化和绘图(SLAM)是从当地观测建立全球环境模型的过程;这是移动机器人的基本能力,支持规划、导航和控制等核心职能;本篇文章回顾了SLAM最近的进展,重点是SLAM系统(代表性)所用环境模型的表达能力的进步,以及用于根据数据(推断)估计这些模型的算法的性能;最近SLAM研究的一个突出主题是寻求环境代表(包括经学习的表示),超越了测地和外观的典型特征,以模拟诸如等级组织、支付能力、动态和语义等特性;这些进步使自主代理更全面地了解世界,使行动更加灵活和智能;第二个主要主题是重新关注SLAM系统估算问题本身的数学属性(包括其计算和信息理论性性性能限制);这项工作导致开发了新颖的可证实性和稳健的推论方法,大大改进了SLAM系统在现实世界操作中的可靠性;这些进步使自主代理更能更全面地了解世界,使得能够更灵活和明智地运作;我们从长远的角度考察这些进展,从研究角度分析这些进展。