JACIII Vol.11 No.6 pp. 688-700
doi: 10.20965/jaciii.2007.p0688


Multiresolutional Fusion of Perceptions Applied to Robot Navigation

Özer Ciftcioglu, Michael S. Bittermann, and I. Sevil Sariyildiz

Chair Design & Informatics, Delft University of Technology, Berlageweg 1, 2628 CR Delft, The Netherlands

January 16, 2007
March 20, 2007
July 20, 2007
perceptual robotics, robot navigation, multiresolution, Kalman filtering, information fusion
Visual perception-based autonomously moving virtual agent in virtual reality as a counterpart of an actual robot moving with a given dynamics is investigated. The visual perception is mathematically modelled as a probabilistic process obtaining and interpreting visual information from an environment. The perception obtained in the form of measurements in 2D is used for perceptual robot navigation. By means of this twofold gain is obtained; while the autonomous robot is navigated, it is equipped with some human-like behaviour, thereby dealing with complexity and environmental dynamics. The visual data is processed in a multiresolutional form via wavelet transform and optimally estimated via extended Kalman filtering in each resolution level and the outcomes are fused for improved estimation of the trajectory. The perceptual robotics experiments are carried out in virtual reality for the demonstration of the feasibility of the investigations in this domain. The computer experiments are carried out with perception measurement data, and the sensor/data fusion experiments are carried out by means of simulation. The improvement on the trajectory estimation by means of sensor/data fusion is demonstrated. The research is connected to building technological robotics, where some form of perceptual intelligence, like reaction to moving objects around, is required during operation.
Cite this article as:
. Ciftcioglu, M. Bittermann, and I. Sariyildiz, “Multiresolutional Fusion of Perceptions Applied to Robot Navigation,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.6, pp. 688-700, 2007.
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