SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Sparse on-line Gaussian processes
Neural Computation
Constructing 3D City Models by Merging Aerial and Ground Views
IEEE Computer Graphics and Applications
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Gaussian process dynamic programming
Neurocomputing
The New College Vision and Laser Data Set
International Journal of Robotics Research
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
Gaussian process modeling of large scale terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Adaptive compression for 3D laser data
International Journal of Robotics Research
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This paper concerns the creation of an efficient, continuous, non-parametric representation of surfaces implicit in 3D laser data as typically recorded by mobile robots. Our approach explicitly leverages the probabilistic nature of Gaussian Process regression to provide for a principled, adaptive subsampling which automatically prunes redundant data. The algorithm places no restriction on the complexity of the underlying surfaces and enables predictions at arbitrary locations and densities. We present results using real and synthetic data and show that our approach attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity of the workspace reconstructions.