Perception planning for a multi-sensory interpretation machine

The authors present a method for selecting viewpoints and sensing tasks to confirm by a multisensory perception machine, an identification hypothesis previously generated. The determination relies on the use of a compiled knowledge base that links object and sensor models, and defines a priori the best sensing tasks to be performed. The method is fully detailed, and its use under dynamic constraints due to a real environment is explained, along with a control strategy to activate the search. Result of experiments integrating other modules for environment modeling, path planning and execution control, and object recognition are presented