TY - CHAP A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Kumar, Suresh A1 - Upcroft, Ben A1 - Williams, Stefan T1 - Operators as information sources in sensor networks T2 - 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems N2 - 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. Y1 - 2005 U6 - https://doi.org/10.1109/IROS.2005.1545015 SP - 936 EP - 941 PB - IEEE ER - TY - CHAP A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Ramos, Fabio A1 - Upcroft, Ben A1 - Williams, Stefan A1 - Durrant-Whyte, Hugh T1 - Adaptive human sensor model in sensor networks T2 - 2005 7th International Conference on Information Fusion N2 - 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. Y1 - 2005 U6 - https://doi.org/10.1109/ICIF.2005.1591929 VL - 1 PB - IEEE ER -