An event-camera-only perception pipeline detects table tennis balls at about 4,140 updates per second, around 28 times the rate of a frame-based baseline, with comparable pixel accuracy.
A multi-modal table tennis robot system
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In recent years, robotic table tennis has become a popular research challenge for perception and robot control. Here, we present an improved table tennis robot system with high accuracy vision detection and fast robot reaction. Based on previous work, our system contains a KUKA robot arm with 6 DOF, with four frame-based cameras and two additional event-based cameras. We developed a novel calibration approach to calibrate this multimodal perception system. For table tennis, spin estimation is crucial. Therefore, we introduced a novel, and more accurate spin estimation approach. Finally, we show how combining the output of an event-based camera and a Spiking Neural Network (SNN) can be used for accurate ball detection.
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
An Event-Based Perception Pipeline for a Table Tennis Robot
An event-camera-only perception pipeline detects table tennis balls at about 4,140 updates per second, around 28 times the rate of a frame-based baseline, with comparable pixel accuracy.