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

DOI10.1002/rob.20308zbMath1243.68295OpenAlexW3083605786MaRDI QIDQ2899385

Nicholas Roy, Christian Plagemann, Kristian Kersting, Sebastian Mischke, Sam Prentice, Wolfram Burgard

Publication date: 30 July 2012

Published in: Journal of Field Robotics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1721.1/59805

Mathematics Subject Classification ID

Robot dynamics and control of rigid bodies (70E60) Artificial intelligence for robotics (68T40)

Related Items (6)

RGB-D terrain perception and dense mapping for legged robots ⋮ A Bayesian regression approach to terrain mapping and an application to legged robot locomotion ⋮ Gaussian process modeling of large-scale terrain ⋮ Rough terrain mapping and classification for foothold selection in a walking robot ⋮ An approach to stable walking over uneven terrain using a reflex-based adaptive gait ⋮ Recurrence plot and convolutional neural networks for terrain classification using energy consumption of multi-legged robots

Uses Software

• Qhull
• LWPR

Cites Work

• Optimization by Simulated Annealing
• Bayesian curve-fitting with free-knot splines
• A Bayesian regression approach to terrain mapping and an application to legged robot locomotion
• The quickhull algorithm for convex hulls
• Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
• A column pre-ordering strategy for the unsymmetric-pattern multifrontal method
• Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes
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