PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian regression approach to terrain mapping and an application to legged robot locomotion
Christian Plagemann, Sebastian Mischke, Sam Prentice, Kristian Kersting, Nicholas Roy and Wolfram Burgard
Journal of Field Robotics Volume 26, Number 10, pp. 789-811, 2009.

Abstract

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 of the model, which makes the model applicable to real-world data sets. The main benefits of our model are that (1) it does not require a discretization of space, (2) it also provides the uncertainty for its predictions, and (3) it adapts its covariance function to the observed data, allowing more accurate inference of terrain elevation at points that have not been observed directly. As a second contribution, we describe how a legged robot equipped with a laser range finder can utilize the developed terrain model to plan and execute a path over rough terrain. We show how a motion planner can use the learned terrain model to plan a path to a goal location, using a terrain-specific cost model to accept or reject candidate footholds. To the best of our knowledge, this was the first legged robotics system to autonomously sense, plan, and traverse a terrain surface of the given complexity.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6501
Deposited By:Kristian Kersting
Deposited On:08 March 2010