Autonomous Mobile Robot Navigation Independent of Road Boundary Using Driving Recommendation Map

Numerous autonomous navigation systems have been proposed, so far, for use in walking environments. Of these, systems that do not rely on high-definition maps and precise localization are cheaper to maintain and easier to implement in unknown outdoor environments. In these systems, road-following navigation using road boundaries is commonly used. In outdoor environments, however, the road boundary is indistinct in some cases. Therefore, it is necessary to infer an area recommended by traffic rules and navigate based on this inference. This paper proposes an autonomous navigation system that is independent of the road boundaries by using a driving recommendation map. In this system, the driving recommendation degree is inferred by deep learning-based semantic segmentation using automatically labeled training data, and a map that probabilistically represents the driving recommendation degree is generated using this information. The proposed autonomous navigation system has demonstrated its usefulness in an environment with indistinct road boundaries. The demonstration shows the proposed autonomous navigation system can plan a path based on driving recommendation degree and properly drive in a scene where the road boundary is indistinct.