M Siva Karthik∗ Sudhanshu Mittal† K. Madhava Krishna‡
In indoor environments, there would be several small objects lying around on the floor. In this work, we develop an efficient strategy to search for a set of queried objects amongst a large number of small objects lying around. Small objects of the order of 1cm − 5cm, appear very small, making it difficult for the present algorithms to recognize them from far away. A human like strategy in such cases is to infer each object’s similarity to the queried objects, from far away. Subsequently, the objects of interest are approached and analyzed from a closer proximity through an optimal plan. We develop an optimal plan for the robot, to strategically visit a selected few among all the objects. From far away, we assign Existential Probabilities to the objects, indicating their similarity to queried objects. A Bayes’ Net is constructed over the probabilities, to overlay and orient a Viewpoint Object Potential(VOP) map over potential search objects. VOP quantifies the probability of accurately recognizing an object through its RGB-D Point Cloud at various viewpoints. The belief from the Bayes’ Net and the discriminative viewpoints from the VOP are utilized to formulate a Decision Tree which helps in building an optimal control plan. Hence, the robot reaches strategic viewpoints around potential objects, to recognize them through their RGB-D point clouds. The framework is experimentally evaluated using Kinect mounted on a Turtlebot using ROS platform.