  Robot table tennis systems require a vision system that can track the ball
position with low latency and high sampling rate. Altering the ball to simplify
the tracking using for instance infrared coating changes the physics of the
ball trajectory. As a result, table tennis systems use custom tracking systems
to track the ball based on heuristic algorithms respecting the real time
constrains applied to RGB images captured with a set of cameras. However, these
heuristic algorithms often report erroneous ball positions, and the table
tennis policies typically need to incorporate additional heuristics to detect
and possibly correct outliers. In this paper, we propose a vision system for
object detection and tracking that focus on reliability while providing real
time performance. Our assumption is that by using multiple cameras, we can find
and discard the errors obtained in the object detection phase by checking for
consistency with the positions reported by other cameras. We provide an open
source implementation of the proposed tracking system to simplify future
research in robot table tennis or related tracking applications with strong
real time requirements. We evaluate the proposed system thoroughly in
simulation and in the real system, outperforming previous work. Furthermore, we
show that the accuracy and robustness of the proposed system increases as more
cameras are added. Finally, we evaluate the table tennis playing performance of
an existing method in the real robot using the proposed vision system. We
measure a slight increase in performance compared to a previous vision system
even after removing all the heuristics previously present to filter out
erroneous ball observations.
