Objects Search Recognition and Localization for Indoor Service Robot Based on Identification Belonging Relationship

Whether or not an indoor service robot can quickly and accurately search for objects is the key to completing the objects delivery task.Due to the location uncertainty of the objects to be searched,it is difficult to obtain the objects semantic and location information accurately and in real time at a long distance.To solve the problem,this paper proposes a vision-based real-time identification and location system that locates belonging objects by identification objects by simulating human’s hierarchical perception of environment and combining location structure information between objects.Firstly,extracting ground in the scene by using the PCA and centroid coordinate,using the ground convex hull and improved region growth segmentation algorithm to achieve bottom-up clustering.Then the CVFH features are used to identify the identification objects in the identification database.After using the returned centroid to obtain the point cloud image for near distance,repeating the above steps to identify and locate the belonging objects in the child belonging database selected based on the identification belonging relationship.At last,experiment and analysis are carried out on the“Turtlebot”service robot platform to verify the effectiveness of the algorithm.