Model-based next view planning by using rules-automatic feature prediction and detection

This paper proposes an effective next view planning strategy for the object recognition and localization task in a model-based robot vision system. A set of rules are designed to automatically predict new features and calculate the next optimal placement of the sensor so that the most useful information can be gathered from multi-views. A state vector (i,r,t) is defined to describe the current state of the vision system and each possible state corresponds to a subset of rules to deal with it. The recognition and location task can be described as a process of rule calling and state conversions. The most suitable rule is selected at each step to try to acquire more useful information as soon as possible. Experiments are shown in the paper