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...

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书目详细资料
Main Authors: Plagemann, Christian, Mischke, Sebastian, Prentice, Samuel James, Kersting, Kristian, Roy, Nicholas
其他作者: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
格式: 文件
语言:en_US
出版: Wiley Periodicals, Inc. 2010
在线阅读:http://hdl.handle.net/1721.1/59805
https://orcid.org/0000-0002-4959-7368
https://orcid.org/0000-0002-8293-0492