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

Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.Comment: 19 pages, 11 figure

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

Computer-aided exploration of architectural design spaces: a digital sketchbook

Het ontwerpproces van architecten vormt vaak geen lineair pad van ontwerpopgave tot eindresultaat, maar wordt veeleer gekenmerkt door exploratie of het doorzoeken van meerdere alternatieven in een (conceptuele) ontwerpruimte. Dit proces wordt in de praktijk vaak ondersteund door manueel schetsen, waarbij de ontwerpers schetsboek kan gelezen worden als een reeks exploraties. Dit soort interactie met de ontwerpruimte wordt in veel mindere mate ondersteund door hedendaagse computerondersteunde ontwerpsystemen. De metafoor van een digitaal schetsboek, waarbij menselijke exploratie wordt versterkt door de (reken)kracht van een computer, is het centrale onderzoeksthema van dit proefschrift. Hoewel het opzet van een ontwerpruimte op het eerste gezicht schatplichtig lijkt aan het onderzoeksveld van de artificiële intelligentie (AI), wordt het ontwerpen hier ruimer geïnterpreteerd dan het oplossen van problemen. Als onderzoeksmethodologie worden vormengrammatica’s ingezet, die enerzijds nauw aanleunen bij de AI en een formeel raamwerk bieden voor de exploratie van ontwerpruimtes, maar tegelijkertijd ook weerstand bieden tegen de AI en een vorm van visueel denken en ambiguïteit toelaten. De twee bijhorende onderzoeksvragen zijn hoe deze vormengrammatica’s digitaal kunnen worden gerepresenteerd, en op welke manier de ontwerper-computer interactie kan gebeuren. De resultaten van deze twee onderzoeksvragen vormen de basis van een nieuw hulpmiddel voor architecten: het digitaal schetsboek

Risk mitigation decisions for it security

Enterprises must manage their information risk as part of their larger operational risk management program. Managers must choose how to control for such information risk. This article defines the flow risk reduction problem and presents a formal model using a workflow framework. Three different control placement methods are introduced to solve the problem, and a comparative analysis is presented using a robust test set of 162 simulations. One year of simulated attacks is used to validate the quality of the solutions. We find that the math programming control placement method yields substantial improvements in terms of risk reduction and risk reduction on investment when compared to heuristics that would typically be used by managers to solve the problem. The contribution of this research is to provide managers with methods to substantially reduce information and security risks, while obtaining significantly better returns on their security investments. By using a workflow approach to control placement, which guides the manager to examine the entire infrastructure in a holistic manner, this research is unique in that it enables information risk to be examined strategically. © 2014 ACM

On the genericity properties in networked estimation: Topology design and sensor placement

In this paper, we consider networked estimation of linear, discrete-time dynamical systems monitored by a network of agents. In order to minimize the power requirement at the (possibly, battery-operated) agents, we require that the agents can exchange information with their neighbors only \emph{once per dynamical system time-step}; in contrast to consensus-based estimation where the agents exchange information until they reach a consensus. It can be verified that with this restriction on information exchange, measurement fusion alone results in an unbounded estimation error at every such agent that does not have an observable set of measurements in its neighborhood. To over come this challenge, state-estimate fusion has been proposed to recover the system observability. However, we show that adding state-estimate fusion may not recover observability when the system matrix is structured-rank ($S$-rank) deficient. In this context, we characterize the state-estimate fusion and measurement fusion under both full $S$-rank and $S$-rank deficient system matrices.Comment: submitted for IEEE journal publicatio