Decision theoretic search for small objects through integrating far and near cues

Abstract

In an object search scenario with several small objects spread over a large indoor environment, the robot cannot infer about all of them at once. Pruning the search space is highly desirable in such a case. It has to actively select a course of actions to closely examine a selected set of objects. Here, we model the inferences about far away objects and their viewpoint priors into a decision analytic abstraction to prioritize the waypoints. By selecting objects of interest, a potential field is built over the environment by using Composite Viewpoint Object Potential (CVOP) maps. A CVOP is built using VOP, a framework to identify discriminative viewpoints to recognize small objects having distinctive features only in specific views. Also, a CVOP helps to clearly disambiguate objects which look similar from far away. We formulate a Decision Analysis Graph (DAG) over the above information, to assist the robot in actively navigating and maximize the reward earned. This optimal strategy increases search reliability, even in the presence of similar looking small objects which induce confusion into the agent and simultaneously reduces both time taken and distance travelled. To the best of our knowledge, there is no current unified formulation which addresses indoor object search scenarios in this manner. We evaluate our system over ROS using a TurtleBot mounted with a Kinect.