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

Cesar Cadena Lerma1, Luca Carlone2, Henry Carrillo3, Yasir Latif4, Davide Scaramuzza5, José Neira6, Ian Reid4, John Leonard7

  • 1ETH Zurich
  • 2Massachusetts Institute of Technology
  • 3Universidad Sergio Arboleda
  • 4University of Adelaide
  • 5University and ETH Zurich
  • 6Universidad de Zaragoza
  • 7MIT



TRO Session (Oral)


09:55 - 11:10 | Tue 30 May | Room 4811/4812 | TUA9

Perception and Planning

Full Text


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 authorsÂ’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

Additional Information

No manuscript is included in ICRA proceedings for TRO presentations.


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