期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/394942
出版社：Hindawi Publishing Corporation
摘要：An autonomous robot in an outdoor environment needs to recognize the surrounding environment to move to a desired location safely; that is, a map is needed to classify/perceive the terrain. This paper proposes a method that enables a robot to classify a terrain in various outdoor environments using terrain information that it recognizes without the assistance of a user; then, it creates a three-dimensional (3D) semantic map. The proposed self-supervised learning system stores data on the appearance of the ground data using image features extracted by observing the movement of humans and vehicles while the robot is stopped. It learns about the surrounding environment using a support vector machine with the stored data, which is divided into terrains where people or vehicles have moved and other regions. This makes it possible to learn which terrain an object can travel on using a self-supervised learning and image-processing methods. Then the robot can recognize the current environment and simultaneously build a 3D map using the RGB-D iterative closest point algorithm with a RGB-D sensor (Kinect). To complete the 3D semantic map, it adds semantic terrain information to the map.
