FVO: floor vision aided odometry

Abstract

In many indoor scenarios, such as restaurants, laboratories, and supermarkets, the planar floors are covered with rectangular tiles. We realized that the abundant parallel lines and crossing points formed by tile joints can be used as natural features to assist indoor localization, and thus we propose a novel indoor localization method for mobile robots by fusing odometry and monocular vision. The method comprises three steps. First, the heading and location of the mobile robot are approximately estimated by odometry based on incremental encoders. Second, with the aid of a camera, the lens of which points vertically toward the floor, the odometric heading estimation can be corrected by detecting the relative angle between the robot’s heading and the tile joints. Third, the odometric location estimation is corrected by detecting the perpendicular distance between the image center and the tile joints. As compared with the existing indoor localization methods, the proposed method, called floor vision aided odometry, is not only relatively low in economic cost and computational complexity, but also relatively high in accuracy and robustness. The effectiveness of this method is verified by a real-world experiment based on a differential-drive wheeled mobile robot.