  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.
