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Type: Journal article
Title: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age
Author: Cadena, C.
Carlone, L.
Carrillo, H.
Latif, Y.
Scaramuzza, D.
Neira, J.
Reid, I.
Leonard, J.
Citation: IEEE Transactions on Robotics, 2016; 32(6):1309-1332
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2016
ISSN: 1552-3098
Statement of
Cesar Cadena, Luca Carlone, Henry Carrillo
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?
Keywords: Graph theory; simultaneous location and mapping; service robots; robustness; localization
Rights: Copyright © 2016, IEEE
DOI: 10.1109/TRO.2016.2624754
Grant ID: ARC
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