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Hierarchical Gaussian Processes for Robust and Accurate Map Building

Kim, Soohwan; Kim, Jonghyuk


This paper proposes a new method for building occupancy maps and surface meshes using hierarchical Gaussian processes. Previously, we found that a Gaussian process, one of the state-of-The-Art machine learning techniques for regression and classification, could serve a unified framework for occupancy mapping and surface reconstruction. Particularly, due to its high computational complexity, we partitioned both training and test data into manageable subsets and applied local Gaussian processes....[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: Australasian Conference on Robotics and Automation, ACRA
Access Rights: Open Access


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