IEEE Transactions on Robotics· 2016Q1· Review
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
- 3,317citations
- Q1SCImago
- 2016year
Short summary
This paper reviews the 30-year progress of Simultaneous Localization and Mapping (SLAM), highlighting its transition to industry and outlining future research challenges.
AI-generated from the title and abstract; the full text is not read.
Key points
- SLAM has progressed significantly over 30 years, moving from research to industrial applications.
- Key areas of development include robustness, long-term mapping, semantic representations, and active SLAM.
- The paper critically analyzes open challenges and identifies new research issues in SLAM.
- It addresses fundamental questions about the necessity and completeness of SLAM technology.
AI-generated from the title and abstract; the full text is not read.
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 and consider future directions. 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?
The authors' abstract, as published at the source. IEEE Transactions on Robotics, 2016 · DOI ↗
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Field: Aerospace Engineering
Aerospace EngineeringEngineering