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

An Agent-Based Model of Mortality Shocks, Intergenerational Effects, and Urban Crime

Rational criminals choose crime over lawfulness because it pays better; hence poverty correlates to criminal behavior. This correlation is an insufficient historical explanation. An agent-based model of urban crime, mortality, and exogenous population shocks supplements the standard economic story, closing the gap with an empirical reality that often breaks from trend. Agent decision making within the model is built around a career maximization function, with life expectancy as the key independent variable. Rational choice takes the form of a local information heuristic, resulting in subjectively rational suboptimal decision making. The effects of population shocks are explored using the Crime and Mortality Simulation (CAMSIM), with effects demonstrated to persist across generations. Past social trauma are found to lead to higher crime rates which subsequently decline as the effect degrades, though \'aftershocks\' are often experienced.Agent-Based Model, Crime, Bounded Rationality, Life Expectancy, Rational Choice

Multi Agent Modelling: Evolution and Skull Thickness in Hominids

Within human evolution, the period of Homo Erectus is particularly interesting since in this period, our ancestors have carried thicker skulls than the species both before and after them. There are competing theories as to the reasons of this enlargement and its reversal. One of these is the theory that Homo Erectus males fought for females by clubbing each other on the head. The other one says that due to the fact that Homo Erectus’ did not cook their food at all, they had to have strong jaw muscles attached to ridges on either side of the skull which prohibited brain and skull growth but required the skull to be thick. The re-thinning of the skull on the other hand might be due to the fact that a thick skull provided poor cooling for the brain or that as hominids started using tools to cut their food and using fire to cook it, they did not require the strong jaw muscles anymore and this trait was actually selected against since the brain had a tendency to grow and the ridges and a thick skull were preventing this. In this paper we simulated both the fighting and the diet as ways in which the hominid skull grew thicker. We also added other properties such as cooperation, selfishness and vision to our agents and analyzed their changes over generations. Keywords: Evolution, Skull Thickness, Hominids, Multi-Agent Modeling, Genetic Algorithm