TY - CHAP A1 - Upcroft, Ben A1 - Ridley, Matthew A1 - Ong, Lee Ling A1 - Douillard, Bertrand A1 - Kaupp, Tobias A1 - Kumar, Suresh A1 - Bailey, Tim A1 - Ramos, Fabio A1 - Makarenko, Alexei A1 - Brooks, Alex A1 - Sukkarieh, Salah A1 - Durrant-Whyte, Hugh F. ED - Khatib, Oussama ED - Kumar, Vijay ED - Rus, Daniela T1 - Multi-level state estimation in an outdoor decentralised sensor network T2 - Experimental Robotics: The 10th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR, volume 39) N2 - 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. Y1 - 2008 SN - 978-3-540-77456-3 U6 - https://doi.org/10.1007/978-3-540-77457-0_33 SP - 355 EP - 365 PB - Springer CY - Berlin, Heidelberg ER - TY - JOUR A1 - Kaupp, Tobias A1 - Douillard, Bertrand A1 - Ramos, Fabio A1 - Makarenko, Alexei A1 - Upcroft, Ben T1 - Shared environment representation for a human-robot team performing information fusion JF - Journal of Field Robotics: Special Issue on Teamwork N2 - 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. Y1 - 2007 U6 - https://doi.org/10.1002/rob.20201 VL - 24 IS - 11-12 SP - 911 EP - 942 ER - TY - CHAP A1 - Durrant-Whyte, Hugh A1 - Sukkarieh, Salah A1 - Ramos, Fabio A1 - Bailey, Tim A1 - Kumar, Suresh A1 - Ong, Lee-Ling A1 - Ridley, Matthew A1 - Kaupp, Tobias A1 - Douillard, Bertrand A1 - Upcroft, Ben T1 - Non-gaussian state estimation in an outdoor decentralised sensor network T2 - Proceedings of the 45th IEEE Conference on Decision and Control N2 - 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. Y1 - 2006 U6 - https://doi.org/10.1109/CDC.2006.377533 SP - 366 EP - 372 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kaupp, Tobias A1 - Douillard, Bertrand A1 - Upcroft, Ben A1 - Makarenko, Alexei T1 - Hierarchical environment model for fusing information from human operators and robots T2 - 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems N2 - 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 Y1 - 2006 U6 - https://doi.org/10.1109/IROS.2006.282398 SP - 5837 EP - 5842 PB - IEEE ER - TY - CHAP A1 - Douillard, Bertrand A1 - Upcroft, Ben A1 - Kaupp, Tobias A1 - Ramos, Fabio A1 - Durrant-Whyte, Hugh T1 - Bayesian filtering over compressed appearance states T2 - Proceedings of the Australasian Conference on Robotics and Automation (ACRA) N2 - 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. Y1 - 2007 UR - https://www.researchgate.net/publication/43527752_Bayesian_filtering_over_compressed_appearance_states ER - TY - CHAP A1 - Upcroft, Ben A1 - Ridley, Matthew A1 - Ong, Lee Ling A1 - Douillard, Bertrand A1 - Kaupp, Tobias A1 - Kumar, Suresh A1 - Bailey, Tim A1 - Ramos, Fabio A1 - Makarenko, Alexei A1 - Brooks, Alex A1 - Sukkarieh, Salah A1 - Durrant-Whyte, Hugh F. T1 - Multilevel state estimation in an outdoor decentralised sensor network T2 - 10th International Symposium on Experimental Robotics 2006 (ISER'06), Rio de Janeiro, Brazil Y1 - 2006 ER -