door environments using autonomous vehicles is proposed. A small vehicle with wireless control and image grabbing capabilities is used as a test bed. Three stages of security patrolling are proposed. First, a simple learning strategy is designed for flexible learning of reachable spots and monitored objects in indoor environments. Accordingly, a planned path is obtained, and monitored objects and doors are specified by analyzing user commands. Next, following the learned path, the vehicle can accomplish specified navigation
sessions. Two methods, one for mechanic error correction modeling and the other for vehicle position modification by monitored object positions, are proposed for navigation accuracy maintenance. Finally, an object matching algorithm is used for checking the existence of monitored objects and the status of door opening. Experimental results show the flexibility and feasibility of the proposed approach.
Relation:
Proceedings of 2005 Conference on Computer Vision, Graphics and Image Processing, pp. 811-818, Taipei, Taiwan, Republic of China
