Three Tiered Self-Localization of Two Position Estimation Using Three Dimensional Environment Map and Gyro-Odometry

As in the Tsukuba Challenge, any robot that autonomously moves around outdoors must be capable of accurate self-localization. Among many existing methods for robot self-localization, the most widely used is for the robot to estimate its position by comparing it with prior map data actually acquired using its sensor while it moves around. Although we use such a self-localization method in this study, this paper proposes a new method to improve accuracy in robot self-localization. In environments with few detected objects, a lack of acquired data very likely will lead to a failure in map matching and to erroneous robot self-localization. Therefore, a method for robot self-localization that uses three-dimensional environment maps and gyro-odometry depending on the situation is proposed. Moreover, the effectiveness of the proposed method is confirmed by using data from the 2013 Tsukuba Challenge course.