Improving landmark detection accuracy for self-localization through baseboard recognition

Acquiring information related to the surrounding environment and environmental mapping are important issues for realizing autonomous robot movement in an unknown environment, and these processes require position estimations for self-localization. We have conducted previous research related to self-localization in indoor environments through baseboard recognition. In that method, a mobile robot performs self-localization using camera-acquired images, from which the intersection of baseboards and the vertical lines of attached doorframes are registered as landmarks. However, this method is limited in that self-localization cannot be performed in cases where baseboards are not partially available or where walls and baseboards are the same color. The present research aims at addressing these issues and implementing the method in an actual mobile robot. The implementing robot is allowed to move autonomously, performing mapping and self-localization in real time. Addressing the problem of same-colored walls requires identification of the border between the wall and the baseboard, so we propose an image processing method for identifying these lines. The problem of lack of baseboards is solved through supplemental use of odometry, which does not rely on visual characteristics. The proposed method is experimentally verified through use of our algorithm to recognize baseboards, perform self-localization, and create maps. The results verify that baseboards can be recognized even when they are the same color as walls. We furthermore verify that landmark-mapping performance when moving in hallways is improved over the previous version.