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.

Crowdsourced Semantic Edge Mapping for Autonomous Vehicles Highly accurate maps of the road infrastructure are a crucial cornerstone for selfdriving cars to enable navigation in complex traffic scenarios. Traditional methods for creating detailed maps of road environments involve expensive survey vehicles that cannot keep up with the frequent changes in the road network. In this paper, we propose a novel method to derive detailed highdefinition maps by crowd sourcing data using commodity sensors. Our system uses multisession featurebased visual SLAM to align submaps recorded by individual vehicles on a central backend server. We reconstruct 3D boundaries of road infrastructure elements such as road markings and road boundaries from semantic object contours detected in keyframes by a neural network. The result is a concise map of semantically meaningful objects suitable both for localization and higherlevel planning tasks of automated vehicles. We evaluate our method on realworld data against a globally referenced groundtruth map demonstrating a high level of detail and metric accuracy.