Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

TL;DR

This paper reviews the evolution, current state, and future challenges of SLAM technology, highlighting its progress, applications, and open research questions in robotics and industry.

Contribution

It provides a comprehensive survey of SLAM, including recent advances, challenges, and perspectives, serving as both a tutorial and a position paper.

Findings

SLAM has enabled large-scale real-world applications.

Robustness and scalability remain key challenges.

Open research issues include long-term mapping and semantic integration.

Abstract

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?