Chavez-Garcia, R. OmarGuzzi, JeromeGambardella, Luca M.Giusti, Alessandro2018-03-292018-03-292018-03-292018-02-0510.1109/LRA.2018.2801794https://infoscience.epfl.ch/handle/20.500.14299/145827Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation dataset, and run a real-robot validation in one indoor and one outdoor environment.TrainingLegged locomotionCollision avoidanceRobot sensing systemsEstimationtext::journal::journal article::research article