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Estimation and prediction of multiple flying balls using Probability Hypothesis Density filtering

We describe a method for estimating position and velocity of multiple flying balls for the purpose of robotic ball catching. For this a multi-target recursive Bayes filter, the Gaussian Mixture Probability Hypothesis Density filter (GM-PHD), fed by a circle detector is used. This recently developed filter avoids the need to enumerate all possible data association decisions, making them computationally efficient. Over time, a mixture of Gaussians is propagated as tracks, predicted into the future and then sent to the robot. By learning a prior from training data we are focusing on detections that are likely to lead to a catchable trajectory which increases robustness. We evaluate the tracker's performance by comparing it with ground truth data, assessing tracking performance as well as the prediction precision of single tracks. Reasonable prediction performance is acquired right from the start, leading to a good overall catching rate.