Visual Topological Mapping and Navigation for Mobile Robot in LargeScale Environment Autonomous navigation is a basic prerequisite for mobile robot to realize environmental exploration. Current navigation methods are mainly based on metric maps, which require precise geometric coordinates and lack the capability to efficiently store semantic information of the environment. In this paper, we present a visual topological mapping and navigation method for mobile robot in largescale environment, which is similar to the human navigation system. Topological map represents the environment as a topology diagram with nodes and edges in which the topological nodes record local semantic information of the environment, such as visual features, robot pose and scene properties. In the topological navigation stage, an imagebased Monte Carlo localization is proposed to estimate the semantic pose of robot which can help robot judge whether it has reached the target location more flexibility. Experiments are conducted in real world environments and results indicate that the proposed system exhibits great performance in robustness of navigation.

VisionBased RoadFollowing Using Results of Semantic Segmentation for Autonomous Navigation Recent research into autonomous navigation of a robot have used accurate and dense threedimensional sensors such as 3DLiDAR and RADAR for map building and localization. However, humans move from a given position to their destination without accurate metric maps in urban scenes: a topological map including only landmarks and their connections enables navigation. Our study endeavors to develop a visual navigation scheme based on a topological map, similar to the scheme used by humans, and this paper proposes a unique roadfollowing scheme using a combination of image processing schemes with the results of semantic segmentation. The proposed scheme identifies a target point toward which the robot moves. To confirm the feasibility of the proposed scheme, a moving experiment on a 500meterlong course in our university campus, where several people were moving back and forth, was conducted with a robot named xe2x80x98Emuxe2x80x99 using only three webcams as external sensors. The experimental results demonstrated that the robot controlled by the proposed scheme could navigate the course adequately.