Abstract
Studies on sensor data fusion in autonomous perceptual robotics are described. The visual perception is represented by a probabilistic model, where the model receives and interprets visual data from the environment in real-time. 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. 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. Various forms of sensor-data fusion is described. The perceptual robotics experiments are carried out in virtual reality for the demonstration of the feasibility of the investigations in this domain. The improvement on the trajectory estimation by means of sensor/data fusion is demonstrated.
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