Learning Ground Traversability from Simulations
This work addresses the challenge of enabling mobile robots to navigate unstructured terrain, but it is incremental as it applies existing neural network methods to a specific domain with simulation-based training.
The paper tackles the problem of predicting traversable terrain for mobile ground robots by training a convolutional neural network on simulation data to classify heightmap patches, achieving successful path planning in both simulated and real-world environments.
Mobile 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 datasets, and run a real-robot validation in one indoor and one outdoor environment.