Learning Ground Traversability from Simulations
| Autor: | Jerome Guzzi, R. Omar Chavez-Garcia, Alessandro Giusti, Luca Maria Gambardella |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Traverse Computer science Biomedical Engineering Terrain 02 engineering and technology Computer Science - Robotics Artificial Intelligence Robot sensing systems 0202 electrical engineering electronic engineering information engineering Training Computer vision business.industry Mechanical Engineering Collision avoidance Elevation 021001 nanoscience & nanotechnology Computer Science Applications Human-Computer Interaction Control and Systems Engineering Heightmap Robot Legged locomotion 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence 0210 nano-technology business Classifier (UML) Estimation Robotics (cs.RO) |
| Popis: | 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. Webpage: http://romarcg.xyz/traversability_estimation/ |
| Databáze: | OpenAIRE |
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