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Self-calibration of eye-hand coordination system with decentralized data fusion

A method of automatically reducing uncertainties and calibrating possible biases involved in sensed data and extracted features by a system based on the geometric data fusion is presented. The perception net, as a structural representation of the sensing capabilities of a system, connects features of various levels of abstraction, referred to here as logical sensors, with their functional relationships such as feature transformations, data fusions, and constraints to be satisfied. The net maintains the consistency of logical sensors based on the forward propagation of uncertainties as well as the backward propagation of constraint errors. A novel geometric data fusion algorithm is presented as a unified framework for computing forward and backward propagation through which the net achieves the self-reduction of uncertainties and self- calibration of biases. The effectiveness of the proposed method is validated through simulation.