A Bayesian regression approach to terrain mapping and an application to legged robot locomotion

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. C 2009 Wiley Periodicals, Inc.

Probabilistic terrain modeling of mobile robots for outdoor applications using a scanning laser

“…Elevation map is widely used for outdoor unstructured terrain environment modeling problem [8][9][10][11][12]. This method uses 2-dimensional grid to represent plane location, and for each grid an elevation value is stored to represent the terrain height information, thus it is also referred as 2.5-dimension map.…”

Unified Terrain Mapping Model With Markov Random Fields

“…The measurement uncertainties, including those resulting from the robot and sensor pose errors, are assumed to be relatively constant such that the scan point variance parameter is a function of the terrain class alone. Vasudevan et al [30], [31], Lang et al [32], and Plagemann et al [33] use Gaussian processes (GPs) to construct terrain height models. Assumptions are made that the heights at all terrain locations are jointly Gaussian.…”

Estimate at completion for construction projects Using Evolutionary Gaussian Process Inference Model

“…Engineering projects face myriad uncertainties attributable to chosen construction method as well as environmental and process factors [1,2]. Construction rms typically focus only on budget planning during the initial project stage, which ignores engineering cost changes, information updates and cost management during construction and, in turn, prevents effective project cost control and the identi cation of potential problems.…”