Probabilistic models for robot-based object segmentation

Authors:
Daniel Beale;Pejman Iravani;Peter Hall
Affiliations:
Department of Computer Science, University of Bath, Bath, BA2 7AY, England, United Kingdom;Department of Mechanical Engineering, University of Bath, Bath, BA2 7AY, England, United Kingdom;Department of Computer Science, University of Bath, Bath, BA2 7AY, England, United Kingdom
Venue:
Robotics and Autonomous Systems
Year:
2011

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Abstract

This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot's motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.