Reliable Real Time Ball Tracking for Robot Table Tennis
Sebastian Gomez-Gonzalez, Yassine Nemmour, Bernhard Sch\"olkopf, Jan, Peters

TL;DR
This paper presents a reliable, real-time vision system for robot table tennis that uses multiple cameras to improve ball tracking accuracy and robustness, outperforming previous heuristic-based methods.
Contribution
The paper introduces a multi-camera vision system that enhances reliability and accuracy in real-time ball tracking without relying on heuristics, validated through extensive simulation and real-world experiments.
Findings
Outperforms previous tracking methods in accuracy and robustness.
Adding more cameras improves system performance.
Slightly increases robot table tennis playing performance.
Abstract
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…
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