Abstract
Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers. These properties make them ideal tools for real-time perception tasks in highly dynamic environments. In this work, we demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency, as well as a learning-based approach that allows real-time inference of a confidence-enabled control signal to the robot. To validate our approach, we present an experimental catching system in which we catch fast-flying ping-pong balls. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms such as the Nvidia Jetson NX.
Original language | English (US) |
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Pages (from-to) | 8737-8744 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 7 |
Issue number | 4 |
DOIs | |
State | Published - Oct 1 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Sensor-based control
- visual tracking