Hierarchical Gaussian Processes for Robust and Accurate Map Building
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]
|Collections||ANU Research Publications|
|Source:||Australasian Conference on Robotics and Automation, ACRA|
|Access Rights:||Open Access|
|01_Kim_Hierarchical_Gaussian_2015.pdf||1.16 MB||Adobe PDF|
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