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?

ResumenEn este reporte se estudia el cálculo del criterio de optimalidad D, para el caso en que es utilizado como una medida de la incertidumbre de un sistema SLAM. Propiedades del uso de este criterio de medida de la incertidumbre en el contexto de SLAM activo son presentadas, al igual que una comparación contra otros criterios de medida de la incertidumbre tales como la entropía y el criterio de optimalidad A. En este reporte se muestra que contrario a lo divulgado previamente en la literatura científica relacionada, el criterio de optimalidad D es capaz de proporcionar informaciónútil acerca de la incertidumbre que tiene un robot que ejecuta un algoritmo de SLAM. Finalmente, a través de varios experimentos con robots reales y simulados, damos soporte a nuestras afirmaciones y mostramos que el uso del criterio de optimalidad D tiene efecto deseables en varias tareas que hacen uso de algoritmos de SLAM como mapeo y navegación activa. AbstractIn this report, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This report shows that contrary to what has been previously reported in the literature, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.

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