  This work presents a non-parametric spatio-temporal model for mapping human
activity by mobile autonomous robots in a long-term context. Based on
Variational Gaussian Process Regression, the model incorporates prior
information of spatial and temporal-periodic dependencies to create a
continuous representation of human occurrences. The inhomogeneous data
distribution resulting from movements of the robot is included in the model via
a heteroscedastic likelihood function and can be accounted for as predictive
uncertainty. Using a sparse formulation, data sets over multiple weeks and
several hundred square meters can be used for model creation. The experimental
evaluation, based on multi-week data sets, demonstrates that the proposed
approach outperforms the state of the art both in terms of predictive quality
and subsequent path planning.
