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Gpmaps: a bayesian nonparametric approach for mapping and reconstruction

Kim, Soohwan

Description

This thesis proposes new methods for robotic mapping using Bayesian nonparametric models such as Gaussian processes and Dirichlet processes. Particularly, we propose a unified framework for occupancy mapping and surface reconstruction using Gaussian processes which are called GPmaps. However, since Gaussian processes suffer from high computational complexity, it is not directly applicable to large-scale environmental mapping. Therefore, we take a divide and conquer strategy by introducing three...[Show more]

CollectionsOpen Access Theses
Date published: 2015
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/111447

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