Probabilistic models for robot-based object segmentation
This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robots 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.
probabilistic modelling active segmentation pattern recognition robot vision sensor fusion robot learning