Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Event-driven Vision and Control for UAVs on a Neuromorphic Chip |
| Organization Unit | |
| Authors |
|
| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| ISBN | 978-1-7281-9077-8 |
| Page Range | 103 - 109 |
| Event Title | 2021 IEEE International Conference on Robotics and Automation (ICRA) |
| Event Type | conference |
| Event Location | Xi'an, China |
| Event Start Date | June 30 - 2021 |
| Event End Date | July 5 - 2021 |
| Publisher | IEEE |
| Abstract Text | Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller on chip solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops. |
| Related URLs | |
| Digital Object Identifier | 10.1109/ICRA48506.2021.9560881 |
| Other Identification Number | merlin-id:22169 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|