Stabilization of stochastic linear continuous-time systems using noisy neuromorphic vision sensors

Prince Singh, Sze Yong, Emilio Frazzoli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Vision-based robotic applications with aggressive maneuvers suffer from the low sensing speed of standard cameras that sample frames at constant time intervals. On the other hand, although neuromorphic vision sensors are promising candidates to provide the needed high-frequency sensing, a new class of algorithms needs to be synthesized that can deal with the uncommon output from each pixel of these sensors, which (independently of other pixels) fire an asynchronous stream of 'retinal events' once a change in the light field is detected. In this paper, we investigate the problem of stabilizing a stochastic continuous-time linear time invariant system using noisy measurements from a neuromorphic vision sensor. We propose an H controller that addresses this problem and provide the critical event-generation threshold for these neuromorphic vision sensors and characterize the statistical properties of the resulting states. The efficacy of our approach is illustrated on an unstable system.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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