Sensing Strategies for Disambiguating among Multiple Objects in Known Poses.
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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The need for intelligent interactions of a robot with its environment frequently requires sensing of the environment. Further, the need for rapid execution requires that the interaction between sensing and action take place using as little sensory data as possible, while still being reliable. Previous work has developed a technique for rapidly determining the feasible poses of an object from sparse, noisy, occluded sensory data. This paper examines techniques for acquiring position and surface orientation data about points on the surface of objects, with the intent of selecting sensory points that will force a unique interpretation of the pose of the object with as few data points as possible. Under some simple assumptions about the sensing geometry a technique for predicting optimal sensing positions is derived. The technique has been implemented and tested. To fully specify the algorithm, needed are estimates of the error in estimating the position and orientation of the object, and derived are analytic expressions for such error for the case of one particular approach to object recognition. Author
- Theoretical Mathematics