Robot methods for human-robot spatial language interaction

Abstract

This thesis talks about a work to design a robot with some to interact with human by spatial language. The robot is a differential drive robot with Kinect camera. The thesis proposes the perception methods which include furniture recognition, furniture orientation detection and robot reposition for recognition performance improvement. The perception uses RGB-Depth image and extracts furniture samples and recognize them by using linguistic model and probability model. A novel method is designed for furniture position and orientation detection. The thesis also shows a method of using robot reposition to improve the recognition performance. The thesis also talks on human robot interaction. It gives a model which can convert human natural spatial language to robot navigation instructions. Several experiments in both physical world and simulation are run to test the efficiency of these algorithms.