Street Floor Segmentation for a Wheeled Mobile Robot

In urban cities, the information about the type or class of street floors enables a wheeled mobile robot to perform many tasks ranging from traversability region identification, localization and the choice of wheel control strategy. In this paper, we considered a new task named as street floor segmentation (SFS) using an RGB camera. The SFS can be considered as the generalized problem of the existing traversability region identification problem in urban situations. Our SFS has two special classes for the possible application to the traversability region identification and they are traversable and non-traversable curbs. The SFS using an RGB camera is implemented using a real-time semantic segmentation (SS) network. A booster module named as Dynamic Context-based Refinement Module (DCRM) was developed to enhance the performance of the SFS. Our network was applied to real-world applications, and its validity is demonstrated through experiment.