Vivekanandan Suryamurthy, Vignesh Sushrutha Raghavan, Arturo Laurenzi, Nikos G. Tsagarakis, and Dimitrios Kanoulas
Abstract— Robots operating in real world environments re- quire 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.
Paper: PDF
Dataset: IIT-TsRe Dataset
Code: https://github.com/dkanou/tsrenet.git
Video: https://youtu.be/QSdB3DBuiu4