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

Learning emergence: adaptive cellular automata façade trained by artificial neural networks

This thesis looks into the possibilities of controlling the emergent behaviour of Cellular Automata (CA) to achieve specific architectural goals. More explicitly, the objective is to develop a performing, adaptive building facade, which is fed with the history of its achievements and errors, to provide optimum light conditions in buildings’ interiors. To achieve that, an artificial Neural Network (NN) is implemented. However, can an artificial NN cope with the complexity of such an emergent system? Moreover, can such a system be trained to compute and yield patterns with specific regional optima, using simple inputs deriving from its environment? Both Backpropagation and optimisation using Genetic Algorithms (GA) are tested to reassign the weights of the network and several experiments are conducted regarding the structure and complexity of both CA and NN. Here it is argued that in fact, it is possible to train such a system although the level of success is strongly dependent on the degree of complexity and the level of resolution and accuracy. By taking advantage of the structural attributes of certain CA that go beyond just a higher order stability, this dissertation suggests that such an evolutionary, computational approach can lead to adaptive and performative architectural spaces of high aesthetic value

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Aesthetically driven design of network based multi-user instruments.

Digital networking technologies open up a new world of possibilities for music making, allowing performers to collaborate in ways not possible before. Network based Multi-User Instruments (NMIs) are one novel method of musical collaboration that take advantage of networking technology. NMIs are digital musical instruments that exist as a single entity instantiated over several nodes in a network and are performed simultaneously by multiple musicians in realtime. This new avenue is exciting, but it begs the question of how does one design instruments for this new medium? This research explores the use of an aesthetically driven design process to guide the design, construction, rehearsal, and performance of a series of NMIs. This is an iterative process that makes use of a regularly rehearsing and performing ensemble which serves as a test-bed for new instruments, from conception, to design, to implementation, to performance. This research includes details of several NMIs constructed in accordance with this design process. These NMIs have been quantitatively analysed and empirically tested for the presence of interconnectivity and group influence during performance as a method for measuring group collaboration. Furthermore qualitative analyses are applied which test for the perceived e ectiveness of these instruments during real-world performances in front of live audiences. The results of these analyses show that an aesthetically driven method of designing NMIs produces instruments that are interactive and collaborative. Furthermore results show that audiences perceive a measurable impression of interconnectivity and liveness in the ensemble even though most of the performers in the ensemble are not physically present

Decentralized data fusion and data harvesting framework for heterogeneous dynamic network systems

Diese Dissertation behandelt das Thema der dezentralisieren Sammlung und Fusion von Daten in heterogenen, ressourcenbeschraekten und dynamischen Netzwerkszenarien. Dazu wird ein generisches Framework vorgestellt, dass es erlaubt die Datensammlung, den Datenaustausch und auch die Datenfusion dynamisch zu konfigurieren. Im Zuge dessen wird auch eine Methode zur gerichteten Fusion von Daten auf graphentheoretischer Basis eingefrt, die es erlaubt eine logische Struktur fuer die Fusion von Informationen zu modellieren. Eine Markup-Sprache, die sowohl menschen- als auch maschinenlesbar ist, erlaubt es diese Struktur leicht zu editieren. Im Bereich der Protokolle zum Datenaustausch liegt der Fokus dieser Arbeit auf Energieeffizienz, um auch ressourcenbeschraenkte Geraete einzubinden. Ein weiterer Schwerpunkt liegt auf Robustheit fuer die betrachteten dynamischen Szenarien. Diese Dissertation schlaet zudem Design-Richtlinien vor, um verschiedene Ziele fuer unterschiedliche Applikationen umzusetzen. Diese lassen sich leicht in das vorgestellte Framework integrieren und darueber konfigurieren. Dadurch ergibt sich im Ganzen eine flexible Architektur, die sich leicht an dynamische Umgebungen anpassen laesst.With the increasing number of available smart phones, sensor nodes, and novel mobile smart devices such as Google glass, a large volume of data reflecting the environment is generated in the form of sensing data sources (such as GPS, received signal strength identification, accelerometer, microphone, images, videos and gyroscope, etc.). Some context-aware and data centric applications require the online processing of the data collected. The thesis researches on the decentralized data fusion and data harvesting framework for heterogeneous dynamic network system consisting of various devices with resource constraints. In order to achieve the flexible design, a general architecture is provided while the detailed data fusion and data exchange functions can be dynamically configured. A novel method to use directed fusion graph to model the logical structure of the distributed information fusion architecture is introduced. This directed fusion graph can accurately portray the interconnection among different data fusion components and the data exchange protocols, as well as the detailed data streams. The directed fusion graph is then transformed into a format with marked language, so that both human and machine can easily understand and edit. In the field of data exchange protocols, this thesis targets energy-efficiency considering the resource constraints of the devices and robustness, as the dynamic environment might cause failures to the system. It proposes a refined gossip strategy to reduce retransmission of redundant data. The thesis also suggests a design guideline to achieve different design aims for different applications. These results in this field can be integrated into the framework effortlessly. The configuration mechanism is another feature of this framework. Different from other research work which consider configuration as a post-design work separated from the main design of any middle-ware. This thesis considers the configuration part as another dimension of the framework. The whole strategy in configuration sets up the foundation for the flexible architecture, and makes it easy to adapt to the dynamic environment. The contributions in the above fields lead to a light-weight data fusion and data harvesting framework which can be deployed easily above wireless based, heterogeneous, dynamic network systems, even in extreme conditions, to handle data-centric applications

Second Generation General System Theory: Perspectives in Philosophy and Approaches in Complex Systems

Following the classical work of Norbert Wiener, Ross Ashby, Ludwig von Bertalanffy and many others, the concept of System has been elaborated in different disciplinary fields, allowing interdisciplinary approaches in areas such as Physics, Biology, Chemistry, Cognitive Science, Economics, Engineering, Social Sciences, Mathematics, Medicine, Artificial Intelligence, and Philosophy. The new challenge of Complexity and Emergence has made the concept of System even more relevant to the study of problems with high contextuality. This Special Issue focuses on the nature of new problems arising from the study and modelling of complexity, their eventual common aspects, properties and approaches—already partially considered by different disciplines—as well as focusing on new, possibly unitary, theoretical frameworks. This Special Issue aims to introduce fresh impetus into systems research when the possible detection and correction of mistakes require the development of new knowledge. This book contains contributions presenting new approaches and results, problems and proposals. The context is an interdisciplinary framework dealing, in order, with electronic engineering problems; the problem of the observer; transdisciplinarity; problems of organised complexity; theoretical incompleteness; design of digital systems in a user-centred way; reaction networks as a framework for systems modelling; emergence of a stable system in reaction networks; emergence at the fundamental systems level; behavioural realization of memoryless functions