Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization
This paper presents a novel online sparse Gaussian process (GP) approximation method  that is capable of achieving constant time and memory (i.e., independent of the size of the data) per time step. We theoretically guarantee its predictive performance to be equivalent to that of a sophisticated offline sparse GP approximation method. We empirically demonstrate the practical feasibility of using our online sparse GP approximation method through a real-world persistent mobile robot localization experiment.
KeywordsMobile Robot Gaussian Process Predictive Performance Time Slice Gaussian Process Regression
Unable to display preview. Download preview PDF.
- 3.Xu, N., Low, K.H., Chen, J., Lim, K.K., Özgül, E.B.: GP-Localize: Persistent mobile robot localization using online sparse Gaussian process observation model. In: Proc. AAAI (2014)Google Scholar