  Low latency and accuracy are fundamental requirements when vision is
integrated in robots for high-speed interaction with targets, since they affect
system reliability and stability. In such a scenario, the choice of the sensor
and algorithms is important for the entire control loop. The technology of
event-cameras can guarantee fast visual sensing in dynamic environments, but
requires a tracking algorithm that can keep up with the high data rate induced
by the robot ego-motion while maintaining accuracy and robustness to
distractors. In this paper, we introduce a novel tracking method that leverages
the Exponential Reduced Ordinal Surface (EROS) data representation to decouple
event-by-event processing and tracking computation. The latter is performed
using convolution kernels to detect and follow a circular target moving on a
plane. To benchmark state-of-the-art event-based tracking, we propose the task
of tracking the air hockey puck sliding on a surface, with the future aim of
controlling the iCub robot to reach the target precisely and on time.
Experimental results demonstrate that our algorithm achieves the best
compromise between low latency and tracking accuracy both when the robot is
still and when moving.
