  Estimating the location and orientation of humans is an essential skill for
service and assistive robots. To achieve a reliable estimation in a wide area
such as an apartment, multiple RGBD cameras are frequently used. Firstly, these
setups are relatively expensive. Secondly, they seldom perform an effective
data fusion using the multiple camera sources at an early stage of the
processing pipeline. Occlusions and partial views make this second point very
relevant in these scenarios. The proposal presented in this paper makes use of
graph neural networks to merge the information acquired from multiple camera
sources, achieving a mean absolute error below 125 mm for the location and 10
degrees for the orientation using low-resolution RGB images. The experiments,
conducted in an apartment with three cameras, benchmarked two different graph
neural network implementations and a third architecture based on fully
connected layers. The software used has been released as open-source in a
public repository (https://github.com/vangiel/WheresTheFellow).
