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Fusion Algorithms (fusion + algorithms)
Selected AbstractsShared environment representation for a human-robot team performing information fusionJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 11-12 2007Tobias Kaupp 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. [source] A cooperative perception system for multiple UAVs: Application to automatic detection of forest firesJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 3-4 2006Luis Merino This paper presents a cooperative perception system for multiple heterogeneous unmanned aerial vehicles (UAVs). It considers different kind of sensors: infrared and visual cameras and fire detectors. The system is based on a set of multipurpose low-level image-processing functions including segmentation, stabilization of sequences of images, and geo-referencing, and it also involves data fusion algorithms for cooperative perception. It has been tested in field experiments that pursued autonomous multi-UAV cooperative detection, monitoring, and measurement of forest fires. This paper presents the overall architecture of the perception system, describes some of the implemented cooperative perception techniques, and shows experimental results on automatic forest fire detection and localization with cooperating UAVs. © 2006 Wiley Periodicals, Inc. [source] Elastic image registration of 2-D gels for differential and repeatability studiesPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 1 2008Carlos O. S. Sorzano Dr. Abstract One of the main applications of electrophoretic 2-D gels is the analysis of differential responses between different conditions. For this reason, specific spots are present in one of the images, but not in the other. In some other occasions, the same experiment is repeated between 2 and 12 times in order to increase statistical significance. In both situations, one of the major difficulties of these analysis is that 2-D gels are affected by spatial distortions due to run-time differences and dye-front deformations, resulting in images that are significantly dissimilar not only because of their content, but also because of their geometry. In this technical brief, we show how to use free, state-of-the-art image registration and fusion algorithms developed by us for solving the problem of comparing differential expression profiles, or computing an "average" image from a series of virtually identical gels. [source] Multi-sensor track-to-track fusion via linear minimum variance sense estimatorsASIAN JOURNAL OF CONTROL, Issue 3 2008Li-Wei Fong Abstract An integrated approach that consists of sensor-based filtering algorithms, local processors, and a global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over a certain area. For the sensor tracking systems, each filtering algorithm utilized in the reference Cartesian coordinate system is presented for target tracking, with the radar measuring range, bearing, and elevation angle in the spherical coordinate system (SCS). For the local processors, each track-to-track fusion algorithm is used to merge two tracks representing the same target. The number of 2-combinations of a set with N distinct sensors is considered for central track fusion. For the global processor, the data fusion algorithms, simplified maximum likelihood (SML) estimator and covariance matching method (CMM), based on linear minimum variance (LMV) estimation fusion theory, are developed for use in a centralized track-to-track fusion situation. The resulting global fusers can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed SML estimator has a more robust capability of improving tracking accuracy than the CMM and the LMV estimators. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | ||||||||||