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Formulation of radiometric feasibility measures for feature selection and planning in visual servoing
Farrokh Janabi-Sharifi1, M Ficocelli
1Robotics and Manufacturing Automation Laboratory, Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, M5B 2K3 Canada.
Abstract:
Feature selection and planning are integral parts of visual servoing systems. Because many irrelevant and nonreliable image features usually exist, higher accuracy and robustness can be expected by selecting and planning good features. Assumption of perfect radiometric conditions is common in visual servoing. The following paper discusses the issue of radiometric constraints for feature selection in the context of visual servoing. Here, radiometric constraints are presented and measures are formulated to select the optimal features (in a radiometric sense) from a set of candidate features. Simulation and experimental results verify the effectiveness of the proposed measures.

