Markov random field modeling in image analysis
Markov random field modeling in image analysis
Ladar-Based Discrimination of Grass from Obstacles for Autonomous Navigation
ISER '00 Experimental Robotics VII
Learning a terrain model for autonomous navigation in rough terrain
Learning a terrain model for autonomous navigation in rough terrain
Double Markov random fields and Bayesian image segmentation
IEEE Transactions on Signal Processing
ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
Markov incremental constructions
Proceedings of the twenty-fourth annual symposium on Computational geometry
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
International Journal of Robotics Research
Real-time photorealistic virtualized reality interface for remote mobile robot control
International Journal of Robotics Research
Robotics and Autonomous Systems
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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.