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    <title language="eng">Multi-level state estimation in an outdoor decentralised sensor network</title>
    <abstract language="eng">Decentralised estimation of heterogeneous sensors is performed on an outdoor network. Attributes such as position, appearance, and identity represented by non-Gaussian distributions are used in in the fusion process. It is shown here that real-time decentralised data fusion of non-Gaussian estimates can be used to build rich environmental maps. Human operators are also used as additional sensors in the network to complement robotic information.</abstract>
    <parentTitle language="eng">Experimental Robotics: The 10th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR, volume 39)</parentTitle>
    <identifier type="doi">10.1007/978-3-540-77457-0_33</identifier>
    <identifier type="isbn">978-3-540-77456-3</identifier>
    <enrichment key="opus.import.data">@inproceedingsupcroft2008multi, title=Multi-level state estimation in an outdoor decentralised sensor network, author=Upcroft, Ben and Ridley, Matthew and Ong, Lee Ling and Douillard, Bertrand and Kaupp, Tobias and Kumar, Suresh and Bailey, Tim and Ramos, Fabio and Makarenko, Alexei and Brooks, Alex and others, booktitle=Experimental Robotics: The 10th International Symposium on Experimental Robotics, pages=355–365, year=2008, organization=Springer Berlin Heidelberg</enrichment>
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    <author>Ben Upcroft</author>
    <author>Matthew Ridley</author>
    <author>Lee Ling Ong</author>
    <author>Bertrand Douillard</author>
    <author>Tobias Kaupp</author>
    <author>Suresh Kumar</author>
    <author>Tim Bailey</author>
    <author>Fabio Ramos</author>
    <author>Alexei Makarenko</author>
    <author>Alex Brooks</author>
    <author>Salah Sukkarieh</author>
    <author>Hugh F. Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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  <doc>
    <id>3264</id>
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    <contributingCorporation>Australian Centre for Field Robotics - The University of Sydney</contributingCorporation>
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    <title language="eng">Probabilistic human-robot information fusion</title>
    <abstract language="eng">This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.</abstract>
    <identifier type="url">http://hdl.handle.net/2123/2554</identifier>
    <enrichment key="opus.import.data">@articlekaupp2008probabilistic, title=Probabilistic human-robot information fusion, author=Kaupp, Tobias and others, year=2008, publisher=University of Sydney.</enrichment>
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    <author>Tobias Kaupp</author>
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  <doc>
    <id>3265</id>
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    <publishedYear>2005</publishedYear>
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    <title language="eng">Human sensor model for range observations</title>
    <parentTitle language="eng">IJCAI Workshop Reasoning with Uncertainty in Robotics (RUR) at IJCAI'05</parentTitle>
    <enrichment key="opus.import.data">@inproceedingskaupp2005human, title=Human sensor model for range observations, author=Kaupp, Tobias and Makarenko, Alexei and Ramos, Fabio and Durrant-Whyte, Hugh, booktitle=IJCAI Workshop Reasoning with Uncertainty in Robotics, year=2005</enrichment>
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    <author>Tobias Kaupp</author>
    <author>Alexei Makarenko</author>
    <author>Fabio Ramos</author>
    <author>Hugh Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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  <doc>
    <id>3267</id>
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    <publishedYear>2006</publishedYear>
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    <language>eng</language>
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    <title language="eng">Implementation of an indoor active sensor network</title>
    <abstract language="eng">This paper describes an indoor Active Sensor Network, focussing on the implementation aspects of the system, including communication and the application framework. To make the system description more tangible we describe the latest in a series of indoor experiments implemented using ASN. The task is to detect and map motion of people (and robots) in an office space using a network of 12 stationary sensors. The network was operational for several days, with individual platform coming on and off line. On several occasions the network consisted of 39 components. The paper includes a section on the lessons learned during the project’s design and development which may be applicable to other heterogeneous distributed systems with data-intensive algorithms.</abstract>
    <parentTitle language="eng">Experimental Robotics IX: The 9th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR,volume 21)</parentTitle>
    <identifier type="doi">10.1007/11552246_38</identifier>
    <identifier type="isbn">978-3-540-28816-9</identifier>
    <enrichment key="opus.import.data">@inproceedingsbrooks2006implementation, title=Implementation of an indoor active sensor network, author=Brooks, Alex and Makarenko, Alexei and Kaupp, Tobias and Williams, Stefan and Durrant-Whyte, Hugh, booktitle=Experimental Robotics IX: The 9th International Symposium on Experimental Robotics, pages=397–406, year=2006, organization=Springer Berlin Heidelberg</enrichment>
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    <author>Alex Brooks</author>
    <author>Alexei Makarenko</author>
    <author>Tobias Kaupp</author>
    <author>Stefan Williams</author>
    <author>Hugh Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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    <id>3268</id>
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    <publishedYear>2007</publishedYear>
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    <issue/>
    <volume>30</volume>
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    <publisherPlace>Berlin</publisherPlace>
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    <title language="eng">Software Engineering for Experimental Robotics</title>
    <parentTitle language="eng">Springer Tracts Series in Advanced Robotics</parentTitle>
    <enrichment key="opus.import.data">@articlebrooks2007software, title=Software Engineering for Experimental Robotics, author=Brooks, A and Kaupp, T and Makarenko, A and Williams, S and Oreback, A, journal=Springer Tracts Series in Advanced Robotics). Berlin, Germany: Springer-Verlag, volume=30, pages=231–251, year=2007</enrichment>
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    <author>Alex Brooks</author>
    <author>Tobias Kaupp</author>
    <author>A. Makarenko</author>
    <author>S. Williams</author>
    <author>A. Oreback</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3269</id>
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    <publishedYear>2007</publishedYear>
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    <language>eng</language>
    <pageFirst>231</pageFirst>
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    <title language="eng">Orca: A component model and repository</title>
    <abstract language="eng">This Chapter describes Orca: an open-source project which applies Component-Based Software Engineering principles to robotics. It provides the means for defining and implementing interfaces such that components developed independently are likely to be inter-operable. In addition it provides a repository of free re-useable components. Orca attempts to be widely applicable by imposing minimal design constraints. This Chapter describes lessons learned while using Orca and steps taken to improve the framework based on those lessons. Improvements revolve around middleware issues and the problems encountered while scaling to larger distributed systems. Results are presented from systems that were implemented.</abstract>
    <parentTitle language="eng">Software engineering for experimental robotics ; Springer Tracts in Advanced Robotics (STAR,volume 30)</parentTitle>
    <identifier type="doi">10.1007/978-3-540-68951-5_13</identifier>
    <identifier type="isbn">978-3-540-68949-2</identifier>
    <enrichment key="opus.import.data">@articlebrooks2007orca, title=Orca: A component model and repository, author=Brooks, Alex and Kaupp, Tobias and Makarenko, Alexei and Williams, Stefan and Orebäck, Anders, journal=Software engineering for experimental robotics, volume=30, pages=231–251, year=2007, publisher=Springer Tracts in Advanced Robotics. Springer-Verlag: Berlin Heidelberg …</enrichment>
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    <author>Alex Brooks</author>
    <author>Tobias Kaupp</author>
    <author>Alexei Makarenko</author>
    <author>Stefan Williams</author>
    <author>Anders Orebäck</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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    <id>3273</id>
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    <publishedYear>2022</publishedYear>
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    <language>eng</language>
    <pageFirst>201</pageFirst>
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    <title language="eng">Perspektiveninvariante Inferenz von Eckpunkten in Packmustern von Kartonagen</title>
    <parentTitle language="eng">FORUM BILDVERARBEITUNG 2022 IMAGE PROCESSING FORUM 2022</parentTitle>
    <enrichment key="opus.import.data">@inproceedingsendres2022perspektiveninvariante, title=Perspektiveninvariante Inferenz von Eckpunkten in Packmustern von Kartonagen, author=Endres, Felix and Reinhart, Lucas and Kaupp, Tobias and Willert, Volker, booktitle=FORUM BILDVERARBEITUNG 2022 IMAGE PROCESSING FORUM 2022, pages=201, year=2022</enrichment>
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    <author>Felix Endres</author>
    <author>Lukas Reinhart</author>
    <author>Tobias Kaupp</author>
    <author>Volker Willert</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3252</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
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    <language>eng</language>
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    <contributingCorporation>Australian Robotics and Automation Association (ARAA)</contributingCorporation>
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    <title language="eng">Applying ISOMAP to the learning of hyperspectral image</title>
    <abstract language="eng">In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.</abstract>
    <parentTitle language="eng">Australian Conference on Robotics and Automation (ACRA’05)</parentTitle>
    <identifier type="url">https://eprints.qut.edu.au/40436/</identifier>
    <enrichment key="opus.import.data">@inproceedingswang2005applying, title=Applying ISOMAP to the learning of hyperspectral image, author=Wang, X Rosalind and Kumar, Suresh and Kaupp, Tobias and Upcroft, Ben and Durrant-Whyte, Hugh, booktitle=Australian Conference on Robotics and Automation (ACRA’05), year=2005</enrichment>
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    <author>X. Rosalind Wang</author>
    <author>Suresh Kumar</author>
    <author>Tobias Kaupp</author>
    <author>Ben Upcroft</author>
    <author>Hugh Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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  <doc>
    <id>3253</id>
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    <publishedYear>2005</publishedYear>
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    <language>eng</language>
    <pageFirst>936</pageFirst>
    <pageLast>941</pageLast>
    <pageNumber>6</pageNumber>
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    <title language="eng">Operators as information sources in sensor networks</title>
    <abstract language="deu">This paper presents an approach of integrating human operators into a sensor network formed by a heterogeneous team of unmanned air and ground vehicles. Several objectives of human-network interaction are identified. The main focus of this work is on human-to-network information flow, i.e. human operators are regarded as information sources. It is argued that operators should make raw observations which are converted into the sensor network's common representation by a probabilistic model. The concepts are discussed in the context of an outdoor sensor network under development. Human operators contribute geometric feature information in the form of range and bearing observations. Visual feature properties are specified via meaningful class labels. A sensor model, represented as a Bayesian network, translates label observations into the system's representation. The model is also used to classify features as observed by robotic sensors.</abstract>
    <parentTitle language="eng">2005 IEEE/RSJ International Conference on Intelligent Robots and Systems</parentTitle>
    <identifier type="doi">10.1109/IROS.2005.1545015</identifier>
    <enrichment key="opus.import.data">@inproceedingskaupp2005operators, title=Operators as information sources in sensor networks, author=Kaupp, Tobias and Makarenko, Alexei and Kumar, Suresh and Upcroft, Ben and Williams, Stefan, booktitle=2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages=936–941, year=2005, organization=IEEE</enrichment>
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    <author>Tobias Kaupp</author>
    <author>Alexei Makarenko</author>
    <author>Suresh Kumar</author>
    <author>Ben Upcroft</author>
    <author>Stefan Williams</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
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    <id>3254</id>
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    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2146</pageFirst>
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    <title language="eng">Measuring human-robot team effectiveness to determine an appropriate autonomy level</title>
    <abstract language="eng">This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system's deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study.</abstract>
    <parentTitle language="eng">2008 IEEE International Conference on Robotics and Automation</parentTitle>
    <identifier type="doi">10.1109/ROBOT.2008.4543524</identifier>
    <enrichment key="opus.import.data">@inproceedingskaupp2008measuring, title=Measuring human-robot team effectiveness to determine an appropriate autonomy level, author=Kaupp, Tobias and Makarenko, Alexei, booktitle=2008 IEEE International Conference on Robotics and Automation, pages=2146–2151, year=2008, organization=IEEE</enrichment>
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    <title language="eng">Human-robot interactions in active sensor networks</title>
    <abstract language="eng">This paper considers the modes of interaction between one or several human operators and an active sensor network-a fully decentralized network of sensors some or all of which have actuators and are in that sense active. The primary goal of this study is to investigate the conditions under which the human involvement will not jeopardize scalability of the overall system. Two aspects of human-robot interaction are considered: the ways in which the global view of the system may be conveyed to the operators, and how the operators may influence the behavior of the system during the course of its operation. The results of analysis favor peer-to-peer environment-centric interactions between the operators and the network whereby the humans act as extended sensors and communication nodes of the network itself. Experiments on an indoor active sensor network are described.</abstract>
    <parentTitle language="eng">Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694)</parentTitle>
    <identifier type="doi">10.1109/CIRA.2003.1222097</identifier>
    <enrichment key="opus.import.data">@inproceedingsmakarenko2003human, title=Human-robot interactions in active sensor networks, author=Makarenko, Alexei and Kaupp, Tobias and Grocholsky, Ben and Durrant-Whyte, Hugh, booktitle=Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694), volume=1, pages=247–252, year=2003, organization=IEEE</enrichment>
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    <author>Alexei Makarenko</author>
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    <title language="eng">Decision-theoretic human-robot communication</title>
    <abstract language="eng">Humans and robots need to exchange information if the objective is to achieve a task cooperatively. Two questions are considered in this paper: what type of information to communicate, and how to cope with the limited resources of human operators. Decision-theoretic human-robot communication can provide answers to both questions: the type of information is determined by the underlying probabilistic representation, and value-of-information theory helps decide when it is appropriate to query operators for information. A robot navigation task is used to evaluate the system by comparing it to conventional teleoperation. The results of a user study show that the developed system is superior with respect to performance, operator workload, and usability.</abstract>
    <parentTitle language="eng">Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction</parentTitle>
    <identifier type="doi">https://doi.org/10.1145/1349822.1349835</identifier>
    <enrichment key="opus.import.data">@inproceedingskaupp2008decision, title=Decision-theoretic human-robot communication, author=Kaupp, Tobias and Makarenko, Alexei, booktitle=Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, pages=89–96, year=2008</enrichment>
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    <id>3259</id>
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    <title language="eng">Gearbox: Truly reusable robot software (Poster)</title>
    <parentTitle language="deu">IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008</parentTitle>
    <enrichment key="opus.import.data">@articlebiggsgearbox, title=Gearbox: Truly reusable robot software, author=Biggs, Geoffrey and Makarenko, Alexei and Brooks, Alex and Kaupp, Tobias and Moser, Michael</enrichment>
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    <author>Geoffrey Biggs</author>
    <author>Alexei Makarenko</author>
    <author>Alex Brooks</author>
    <author>Tobias Kaupp</author>
    <author>Michael Moser</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3260</id>
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    <publishedYear>2009</publishedYear>
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    <language>eng</language>
    <pageFirst>222</pageFirst>
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    <pageNumber>6</pageNumber>
    <edition/>
    <issue>20</issue>
    <volume>42</volume>
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    <title language="eng">Decentralised Data Fusion with Dynamic Topologies-A Graphical Model Approach</title>
    <parentTitle language="eng">IFAC Proceedings Volumes</parentTitle>
    <enrichment key="opus.import.data">@articlebrooks2009decentralised, title=Decentralised Data Fusion with Dynamic Topologies-A Graphical Model Approach, author=Brooks, Alex and Makarenko, Alexei and Kaupp, Tobias and Durrant-Whyte, Hugh and Dellaert, Frank, journal=IFAC Proceedings Volumes, volume=42, number=20, pages=222–227, year=2009, publisher=Elsevier</enrichment>
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    <author>Alex Brooks</author>
    <author>Alexei Makarenko</author>
    <author>Tobias Kaupp</author>
    <author>Hugh Durrant-Whyte</author>
    <author>Frank Dellaert</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3261</id>
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    <publishedYear>2007</publishedYear>
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    <language>eng</language>
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    <title language="eng">On the benefits of making robotic software frameworks thin</title>
    <abstract language="eng">Today's reusable robotics software is provided by several self-contained open-source projects with virtually no software reuse between them. Such partitioning leads to problems with software quantity, quality, ease of evaluation and, ultimately, to poor end user experience. By reviewing several of the projects we observe that all of them contain a mix of three types of software: 1) driver and algorithm implementations, 2) communication middleware, and 3) robotic software framework. We show that more than half of the combined code base contains software which could be highly reusable but only a small fraction of it actually is. We argue that formal separation of the three groups in the existing and future software projects would offer several potential advantages. Availability of framework-independent code would enable community-wide library-based software reuse in addition to the existing framework-wide component- based reuse. Another important benefit is related to evaluation procedures. The three software types are very different and should be evaluated separately, using different criteria. The first two types allow quantitative comparisons which are well documented in the literature. The last one is largely qualitative and, therefore, more subjective. Practically, we recommend today's projects to refactor and eject the driver and algorithm implementation code and focus on the task of software integration. Thin frameworks can be numerous, increasing the diversity of options available to robotics practitioners. Finally, we discuss distribution options for the refactored framework-independent code including the option of creating a Common Robotic Project.</abstract>
    <parentTitle language="eng">International Conference on Intelligent Robots and Systems - Workshop for Measures and Procedures for the Evaluation of Robot Architectures and Middleware at IROS'07</parentTitle>
    <enrichment key="opus.import.data">@inproceedingsmakarenko2007benefits, title=On the benefits of making robotic software frameworks thin, author=Makarenko, Alexei and Brooks, Alex and Kaupp, Tobias, booktitle=International Conference on Intelligent Robots and Systems, volume=2, year=2007</enrichment>
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    <author>Alexei Makarenko</author>
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  </doc>
  <doc>
    <id>3262</id>
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    <publishedYear>2009</publishedYear>
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    <publisherName>VDM Verlag</publisherName>
    <publisherPlace>Saarbrücken</publisherPlace>
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    <title language="eng">Human-robot collaboration: a probabilistic approach</title>
    <identifier type="isbn">978-3-6391-2318-0</identifier>
    <enrichment key="opus.import.data">@misckaupp2009human, title=Human-robot collaboration: a Probabilistic approach, author=Kaupp, Tobias, year=2009, publisher=VDM Publishing</enrichment>
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  </doc>
  <doc>
    <id>3233</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>1</volume>
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    <publisherName>IEEE</publisherName>
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    <title language="eng">Adaptive human sensor model in sensor networks</title>
    <abstract language="eng">This paper presents the design of a probabilistic model of human perception as an integral part of a decentralized data fusion system. The system consists of a team of human operators and robotic platforms, together forming a heterogeneous sensor network. Human operators are regarded as information sources submitting raw observations. The observations are converted into a probabilistic representation suitable for fusion with the system's belief. The conversion is performed by a human sensor model (HSM). The initial HSM is built offline based on an average of multiple human subjects conducting a calibration experiment. Since individual human operators may vary in their performance, an online adaptation of the HSM is required. The network estimate is used for adaptation because the true feature state is unknown at runtime. Results of an outdoor calibration experiment using range and bearing observations are presented. Simulations show the feasibility of efficient online adaptation.</abstract>
    <parentTitle language="eng">2005 7th International Conference on Information Fusion</parentTitle>
    <identifier type="doi">10.1109/ICIF.2005.1591929</identifier>
    <enrichment key="opus.import.data">@inproceedingskaupp2005adaptive, title=Adaptive human sensor model in sensor networks, author=Kaupp, Tobias and Makarenko, Alexei and Ramos, Fabio and Upcroft, Ben and Williams, Stefan and Durrant-Whyte, Hugh, booktitle=2005 7th International Conference on Information Fusion, volume=1, pages=8–pp, year=2005, organization=IEEE</enrichment>
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    <author>Tobias Kaupp</author>
    <author>Alexei Makarenko</author>
    <author>Fabio Ramos</author>
    <author>Ben Upcroft</author>
    <author>Stefan Williams</author>
    <author>Hugh Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3234</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>911</pageFirst>
    <pageLast>942</pageLast>
    <pageNumber>32</pageNumber>
    <edition/>
    <issue>11-12</issue>
    <volume>24</volume>
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    <title language="eng">Shared environment representation for a human-robot team performing information fusion</title>
    <abstract language="eng">This paper addresses the problem of building a shared environment representation by a human-robot team. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision-making. Two probabilistic models are used to describe outdoor environment features such as trees: geometric (position in the world) and visual. The visual representation is used to improve data association and to classify features. Both models are able to incorporate observations from robotic platforms and human operators. Physically, humans and robots form a heterogeneous sensor network. In our experiments, the human-robot team consists of an unmanned air vehicle, a ground vehicle, and two human operators. They are deployed for an information gathering task and perform information fusion cooperatively. All aspects of the system including the fusion algorithms are fully decentralized. Experimental results are presented in form of the acquired multi-attribute feature map, information exchange patterns demonstrating human-robot information fusion, and quantitative model evaluation. Learned lessons from deploying the system in the field are also presented. © 2007 Wiley Periodicals, Inc.</abstract>
    <parentTitle language="eng">Journal of Field Robotics: Special Issue on Teamwork</parentTitle>
    <identifier type="doi">10.1002/rob.20201</identifier>
    <enrichment key="opus.import.data">@articlekaupp2007shared, title=Shared environment representation for a human-robot team performing information fusion, author=Kaupp, Tobias and Douillard, Bertrand and Ramos, Fabio and Makarenko, Alexei and Upcroft, Ben, journal=Journal of Field Robotics, volume=24, number=11-12, pages=911–942, year=2007, publisher=Wiley Online Library</enrichment>
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    <author>Tobias Kaupp</author>
    <author>Bertrand Douillard</author>
    <author>Fabio Ramos</author>
    <author>Alexei Makarenko</author>
    <author>Ben Upcroft</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
  <doc>
    <id>3236</id>
    <completedYear/>
    <publishedYear>2003</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>63</pageFirst>
    <pageLast>71</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>2</volume>
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    <title language="eng">Scalable human-robot interactions in active sensor networks</title>
    <abstract language="eng">Decentralized sensor networks promise virtually unlimited scalability and can tolerate individual component failures. Interaction with human operators plays an important role in the overall system design. The authors describe an experimental active sensor network and identify modes of human-robot interaction that keep up with the network's arbitrary growth.</abstract>
    <parentTitle language="eng">IEEE Pervasive Computing</parentTitle>
    <identifier type="doi">10.1109/MPRV.2003.1251170</identifier>
    <enrichment key="opus.import.data">@articlemakarenko2003scalable, title=Scalable human-robot interactions in active sensor networks, author=Makarenko, Alexei A and Kaupp, Tobias and Durrant-Whyte, Hugh F, journal=IEEE Pervasive Computing, volume=2, number=4, pages=63–71, year=2003, publisher=IEEE</enrichment>
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    <author>Alexei A. Makarenko</author>
    <author>Tobias Kaupp</author>
    <author>Hugh F. Durrant-Whyte</author>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
  </doc>
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    <id>3237</id>
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    <publishedYear>2006</publishedYear>
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    <language>eng</language>
    <pageFirst>366</pageFirst>
    <pageLast>372</pageLast>
    <pageNumber>7</pageNumber>
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    <publisherPlace>Piscataway</publisherPlace>
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    <completedDate>--</completedDate>
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    <title language="eng">Non-gaussian state estimation in an outdoor decentralised sensor network</title>
    <abstract language="eng">This paper presents the development and demonstration of non-Gaussian, decentralised state estimation using an outdoor sensor network consisting of an autonomous air vehicle, a manual ground vehicle, and two human operators. The location and appearance of landmarks were estimated using bearing only observations from monocular cameras. We show that inclusion of visual and identity information aids validation gating for data association when geometric information alone cannot discriminate individual landmarks. The combination of geometric, appearance, and identity information provided a common description (or map) of natural features for each of the nodes in the network. We also show the final map from the live demonstration which includes position estimates and classification labels of the observed features.</abstract>
    <parentTitle language="eng">Proceedings of the 45th IEEE Conference on Decision and Control</parentTitle>
    <identifier type="doi">10.1109/CDC.2006.377533</identifier>
    <enrichment key="opus.import.data">@inproceedingsdurrant2006non, title=Non-gaussian state estimation in an outdoor decentralised sensor network, author=Durrant-Whyte, Hugh and Sukkarieh, Salah and Ramos, Fabio and Bailey, Tim and Kumar, Suresh and Ong, Lee-Ling and Ridley, Matthew and Kaupp, Tobias and Douillard, Bertrand and Upcroft, Ben, booktitle=Proceedings of the 45th IEEE Conference on Decision and Control, pages=366–372, year=2006, organization=IEEE</enrichment>
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    <author>Suresh Kumar</author>
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    <title language="eng">Hierarchical environment model for fusing information from human operators and robots</title>
    <abstract language="eng">This paper considers the problem of building environment models by fusing information gathered by robotic platforms with human perceptual information. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision making. Hierarchical models are well suited to represent complex environments because they: offer multiple abstractions of the available information to support analysis and decision-making, and permit the incorporation of higher-level human observations. The contributions of this paper are two-fold: (1) development of a probabilistic three-level environment model for distributed information gathering, and (2) experimental demonstration of fully decentralized, cooperative human-robot information gathering using an outdoor sensor network comprised of an unmanned air vehicle, a ground vehicle, and two human operators. Several information exchange patterns are presented which qualitatively demonstrate human-robot information fusion</abstract>
    <parentTitle language="eng">2006 IEEE/RSJ International Conference on Intelligent Robots and Systems</parentTitle>
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    <title language="eng">Systems and methods for arranging firearms training scenarios (Patent)</title>
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    <title language="eng">Bayesian filtering over compressed appearance states</title>
    <abstract language="eng">This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.</abstract>
    <parentTitle language="eng">Proceedings of the Australasian Conference on Robotics and Automation (ACRA)</parentTitle>
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    <title language="eng">Target device for use in a live fire training exercise and method of operating the target device (Patent)</title>
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    <title language="eng">Building a software architecture for a human-robot team using the orca framework</title>
    <abstract language="eng">This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a component-based software engineering approach can address these constraints. The architecture is implemented using Orca - a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the player software framework in the context of distributed systems.</abstract>
    <parentTitle language="eng">Proceedings 2007 IEEE International Conference on Robotics and Automation</parentTitle>
    <identifier type="doi">10.1109/ROBOT.2007.364051</identifier>
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    <title language="eng">A method of providing a live fire training environment and a moveable target for use therein (Patent)</title>
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    <title language="eng">Scalable control of decentralised sensor platforms</title>
    <abstract language="eng">This paper describes an information-theoretic approach to decentralised and coordinated control of multi-robot sensor systems. It builds on techniques long established for the related problem of Decentralised Data Fusion (DDF). The DDF architecture uses information measures to communicate state estimates in a network of sensors. For coordinated control of robot sensors, the control objective becomes maximisation of these information measures. A decentralised coordinated control architecture is presented. The approach taken seeks to achieve scalable solutions that maintain consistent probabalistic sensor fusion and payoff formulations. It inherits the many benefits of the DDF method including scalability, seamless handling of sub-system activation and deactivation, and interoperability among heterogeneous units. These features are demonstrated through application to practical multi-feature localisation problems on a team of indoor robots equipped with laser range finders.</abstract>
    <parentTitle language="eng">Information Processing in Sensor Networks ; Lecture Notes in Computer Science (LNCS, Volume 2634)</parentTitle>
    <identifier type="doi">10.1007/3-540-36978-3_7</identifier>
    <identifier type="isbn">978-3-540-02111-7</identifier>
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    <title language="eng">Towards component-based robotics</title>
    <abstract language="eng">This paper gives an overview of component-based software engineering (CBSE), motivates its application to the field of mobile robotics, and proposes a particular component model. CBSE is an approach to system-building that aims to shift the emphasis from programming to composing systems from a mixture of off-the-shelf and custom-built software components. This paper argues that robotics is particularly well-suited for and in need of component-based ideas. Furthermore, now is the right time for their introduction. The paper introduces Orca - an open-source component-based software engineering framework proposed for mobile robotics with an associated repository of free, reusable components for building mobile robotic systems.</abstract>
    <parentTitle language="eng">2005 IEEE/RSJ International Conference on Intelligent Robots and Systems</parentTitle>
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    <title language="eng">Randomised MPC-based motion-planning for mobile robot obstacle avoidance</title>
    <abstract language="eng">This paper presents an algorithm for real-time sensor-based motion planning under kinodynamic constraints, in unknown environments. The objective of the trajectory-generation algorithm is to optimise a cost function out to a limited time horizon. The space of control trajectories is searched by expanding a tree using randomised sampling, in a manner similar to an RRT. The algorithm is improved by seeding the tree using the best control trajectory from the previous iteration, and by pruning branches based on a bound to the cost function and the best trajectory found so far. Performance of the algorithm is analysed in simulation. In addition, the algorithm has been implemented on two kinds of vehicles: the Segway RMP and a four-wheel-drive. The algorithm has been used to drive autonomously for a combined total on the order of hundreds of hours.</abstract>
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    <identifier type="doi">10.1109/ROBOT.2009.5152240</identifier>
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    <title language="eng">Decentralised data fusion: A graphical model approach</title>
    <parentTitle language="eng">2009 12th International Conference on Information Fusion</parentTitle>
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