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

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

Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots

International audienceThe exploration of an unknown environment by a robot system (an individual robot or a team of robots) is a well-studied problem in robotics. This problem has many applications and, among them, the post-disaster search of victims in an urban space. Most of proposed exploration algorithms are based on the use of specific criteria to define the quality of the possible movements. In this paper, we propose an exploration approach based on the combination of several criteria thanks to the PROMETHEE II multi-criteria decision making method. The PROMETHEE II method allows one to establish a complete ranking between possible movements based on outranking relations. Experimental results show that this approach can be used to effectively combine different criteria and outperforms several classic exploration strategies