A generative model of terrain for autonomous navigation in vegetation

  • Wellington C
  • Courville A
  • Stentz A
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Current approaches to off-road autonomous navigation are often limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground height and the location of obstacles, especially in domains where vegetation may hide the ground surface or partially obscure obstacles. A generative, probabilistic terrain model is introduced that exploits natural structure found in off-road environments to constrain the problem and use ambiguous sensor data more effectively. The model includes two Markov random fields that encode the assumptions that ground heights smoothly vary and terrain classes tend to cluster. The model also includes a latent variable that encodes the assumption that vegetation of a single type has a similar height. The model parameters can be trained by simply driving through representative terrain. Results from a number of challenging test scenarios in an agricultural domain reveal that exploiting the 3D structure inherent in outdoor domains significantly improves ground estimates and obstacle detection accuracy, and allows the system to infer the supporting ground surface even when it is hidden under dense vegetation.

Author-supplied keywords

  • Autonomous navigation
  • Gibbs sampling
  • Hidden semi-Markov models
  • Learning
  • Markov random fields
  • Probabilistic inference
  • Terrain modeling
  • Vegetation

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  • Carl Wellington

  • Aaron Courville

  • Anthony Stentz

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