Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Parametric Modeling of Spatio-Temporal Human Activity Based on
  Mobile Robot Observations   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.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg