A Bayesian Regression Approach to Terrain Mapping and an Application to Legged Robot Locomotion
We deal with the problem of learning probabilistic models of terrain surfaces from sparse and noisy elevation measurements. The key idea is to formalize this as a regression problem and to derive a solution based on nonstationary Gaussian processes. We describe how to achieve a sparse approximation...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Wiley Periodicals, Inc.
2010
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| Online Access: | http://hdl.handle.net/1721.1/59805 https://orcid.org/0000-0002-4959-7368 https://orcid.org/0000-0002-8293-0492 |