Self-localization And Environment Recognition Of Mobile Robot Based On Unidirectional Vision

With the fast development of new technology in intelligent control, computer science, networking, bionics and artificial intelligence, mobile robot has become the focus in the field of robotics and automation. Self-localization is one of the foremost problems for intelligent navigation and environment exploration. The intension of this paper is to introduce our research on nonholonomic mobile robot self-localization and environment recognition in the indoor environment with uncertainty information.This paper has performed a research on the self-localization based on unidirectional vision for indoor automobiles. The paper firstly expounds state of the art in localization research, and presents the leading methods, key technical issues and future development trends. Then it constructs an effective environment map to describe the robot's working area.Towards large scale corridor environment, a novel metric-topological 3D map is proposed for robot self-localization based on unidirectional vision. The local metric map, in a hierarchical manner, defines geometrical element according to its environmental feature level. Then, the topological parts in global map are used to connect the adjacent local maps. We design a nonlinear unidirectional camera model to project the probabilistic map elements with uncertainty manipulation. For self-localization task, a human-machine interaction system is developed using hierarchical logic. It provides a fusion center which applies feedback hierarchical fusion method to fuse local estimates generated from multi-observations.When the robot travels in an unknown environment, it has very little information about the surrounding. The environment recognition is very important for the environment modeling, self-locating, route planning and other activities. As a new subject, recognition theory of robots, which merges computer science, recognition psychology, neurology and bionics, is widely researched. This paper has discussed some issues of environment recognition, and demonstrated the application of SIFT used in environment recognition.