Rapid and high precision data acquisition methodology from coordinate components with free-form surface and geometrical model can be implemented widely. Typical application covers part localization, automatic calibrat...
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Rapid and high precision data acquisition methodology from coordinate components with free-form surface and geometrical model can be implemented widely. Typical application covers part localization, automatic calibration and reverse engineering. Integrated structured light vision sensor with CMM (Coordinate Measurement Machine) enhances the high-precision coordinate measurement capability. In this paper a curvature-based adaptive sampling approach and the evaluating index for the sampling precision are presented. The matching and subdividing algorithm for generating matrix-type mesh data from sample points is described. The methodology to register and merge the measured data from multiple viewpoints to model the free-form surface is also presented. Based on the given initial coordinate rotation matrix R and transformation vector T, the different viewpoints can be translated into a unique frame of reference. By introducing special coordinate of 3D space, the registered data is divided into mesh, which cover the whole surface of object. An application example for shoe modeling is described to illustrate the advantages.
Insect-inspired sensorfusion algorithms have presented a promising avenue in the development of robust and efficient systems, owing to the insects' ability to process numerous streams of noisy sensory data. The r...
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Insect-inspired sensorfusion algorithms have presented a promising avenue in the development of robust and efficient systems, owing to the insects' ability to process numerous streams of noisy sensory data. The ring attractor neural network architecture has been identified as a noteworthy model for the optimal integration of diverse insect sensors. Expanding on this, our research presents an innovative bio-inspired ring attractor neural network architecture designed to augment the performance of microsatellite attitude determination systems through the fusion of data from multiple gyroscopic *** simulations using a nonlinear model of the microsatellite, while incorporating specific navigational disturbances, have been conducted to ascertain the viability and effectiveness of this approach. The results obtained have been superior to those of alternative methodologies, thus highlighting the potential of our proposed bio-inspired fusion technique. The findings indicate that this approach could significantly improve the accuracy and robustness of microsatellite systems across a wide range of applications.
The authors address the development of a coherent framework suitable for the treatment of the heterogeneous multisensorfusion problem, in the context of a robotic environment. The analysis, is based on a geometric de...
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The authors address the development of a coherent framework suitable for the treatment of the heterogeneous multisensorfusion problem, in the context of a robotic environment. The analysis, is based on a geometric description of the robotic environment, and concentrates on parameterizable features of the assumed rigid dynamic objects of interest present in the environment. A quantitative representation of a sensor's inherent ability to extract pertinent information from the environment is stressed. A highly efficient, flexible, and fault-tolerant, decentralizedsensorfusion architecture based on a linear information type structure is presented and compared with previous work in this area. The inclusion of techniques to cope with generalized spatial and temporal uncollocation, along with conclusive discussions on the most appropriate level in the fusion structure for these alignment procedures to be performed, represent the principal contribution.< >
This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground roboticsensor platforms. The approach taken builds on well known decentralized Data Fus...
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This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground roboticsensor platforms. The approach taken builds on well known decentralized Data fusion (DDF) methodology. In particular, it brings together established representations developed for identification and linearized estimation problems to jointly address feature detection and localization. This provides transparent and scalable integration of sensor information from air and ground platforms. As in previous studies, an Information-theoretic utility measure and local control strategy drive the robots to uncertainty reducing team configurations. Complementary characteristics in terms of coverage and accuracy are revealed through analysis of the observation uncertainty for air and ground on-board cameras. Implementation results for a detection and localization example indicate the ability of this approach to scalably and efficiently realize such collaborative potential.
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