Indoor SLAM based on line observation probability using a hand-drawn map

Abstract In this paper, we propose a novel method to build indoor map information by utilizing a hand-drawn map as prior information is given. So far, previous studies using the hand-drawn map have been limited to robot pose estimation and navigation. In our approach, finding the correspondence between the shape of the real environment and the shape of the hand-drawn map is performed to build accurate map information. In addition, even if the estimation of the robot pose on the hand-drawn map fails, our method can continue to build the map by re-estimating the robot’s pose on the hand-drawn map based on the previous corresponding information. In the simulations, we verified the accuracy of the built map using three hand-drawn maps.