Recognition of object by extended Histograms of Oriented Gradients (EHOG) on route for a mobile robot
Yuri Shimanuki, Koichi Hidaka · 2014
This paper presents a recognition of obstacle and objects for an industrial a mobile robot, e.g., an automated guided vehicle (AGV), by using monocular camera. The mobile robot moves for transporting same parts in a factory where the robot has to pass a production line. An accurate recognition of object on the production line is required for moving the robot automatically. In addition, the robustness to luminance changes is required. During the past decades, some robust features, such as Scale Invariant Feature Transform(SIFT), Speeded Up Robust Features(SURF), Histograms of Oriented Gradients(HOG), or Extended HOG(EHOG), have been proposed in computer vision and machine learning. In this paper, we focus on the robustness of EHOG and we propose a decision algorithm of objects on a path by using the machine learning based on EHOG.We show that experimental results are provided and the usefulness of the proposed algorithm is introduced by these results.