The Landmark Detection Method using SURF and Q-Learning for an Autonomous Mobile Robot

Abstract

It is needs self-localization without GPS, when an autonomous mobile robot is doing in indoor. One of the landmark detection is used self-localization method. Conventional landmark detection methods were used template matching or color data. However, it is difficult to the landmark detection using conventional methods in real environment, because the landmark detection requires a lot of template. In addition, the landmark detection methods proposed using Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). Among them, SURF is strong to scale-change, rotation, and varying illumination. But, the detection methods using features cannot detect the landmark from many features similar to the landmark in real environment. Therefore, in this study, we build several landmark detectors using SURF and environment information is strong to scale-change, rotation, and varying illumination. In addition, we build a rule to select the best detector from these detectors by the reinforcement learning. We examine usefulness of the proposed method by comparing a landmark detection rate of the conventional method that is the AdaBoost detection using Haar-like features. The experiment result shows that the proposed method surpasses the landmark detection rate than the conventional method.