| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 96 === A vision-based vehicle system for security patrolling in indoor environments using an autonomous vehicle is proposed. A small vehicle with wireless control and a web camera which has the capabilities of panning, tilting, and zooming is used as a test bed. At first, an easy-to-use learning technique is proposed, which has the capability of extracting specific features, including navigation path, floor color, monitored object, and vehicle location with respect to monitored objects. Next, a security patrolling method by vehicle navigation with obstacle avoidance and security monitoring capabilities is proposed. The vehicle navigates according to the node data of the path map which is created in the learning phase and monitors concerned objects by a simplified scale-invariant feature transform (simplified-SIFT) algorithm proposed in this study. Accordingly, we can extract the features of each monitored object from acquired images and match them with the corresponding learned data by the Hough transform. Furthermore, a vehicle location estimation technique for path correction utilizing the monitored object matching result is proposed. In addition, techniques for obstacle avoidance are also proposed, which can be used to find the clusters of floor colors, detect obstacles in environments with various floor colors, and integrate a technique of goal-directed minimum path following to guide the vehicle to avoid obstacles. Good experimental results show the flexibility and feasibility of the proposed methods for the application of security patrolling in indoor environments.
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