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V.Suryamurthy*, V.S. Raghavan*, A. Laurenzi, N.G. Tsagarakis, D. Kanoulas: " Terrain Segmenta-tion and Roughness Estimation using RGB Data: Path Planning Application on the CENTAURO Robot", IEEE-RAS Humanoids 2019

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Robots operating in real world environments require a high-level perceptual understanding of the chief physical properties of the terrain they are traversing.  In unknown environments, roughness is one such important terrain property that could play a key role in devising robot control/planning strategies.  In this paper, we present a fast method for predicting pixel-wise labels of terrain (stone, sand, road/sidewalk, wood, grass, metal) and roughness estimation, using a single RGB-based deep neural network.  Real world RGB images are used to experimentally validate the presented approach. Furthermore, we demonstrate an application of our proposed method on the centaur-like wheeled-legged robot CENTAURO, by integrating it with a navigation planner that is capable of re-configuring the leg joints to modify the robot footprint polygon for stability purposes or for safe traversal among obstacles.