Neural Implicit Event Generator for Motion Tracking

Mana Masuda, Yusuke Sekikawa, Ryo Fujii, Hideo Saito

研究成果: Conference contribution

抄録

We present a novel framework of motion tracking from event data using implicit expression. Our framework uses pre-trained event generation MLP called the implicit event generator (IEG) and carries out motion tracking by updating its state (position and velocity) based on the difference between the observed event and generated event from the current state estimation. The difference is computed implicitly by the IEG. Unlike the conventional explicit approach, which requires dense computation to evaluate the difference, our implicit approach realizes the update of the efficient state directly from sparse event data. Our sparse algorithm is especially suitable for mobile robotics applications in which computational resources and battery life are limited. To verify the effectiveness of our method on real-world data, we applied it to the AR marker tracking application. We have confirmed that our framework works well in real-world environments in the presence of noise and background clutter.

本文言語English
ホスト出版物のタイトル2022 IEEE International Conference on Robotics and Automation, ICRA 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2200-2206
ページ数7
ISBN(電子版)9781728196817
DOI
出版ステータスPublished - 2022
イベント39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
継続期間: 2022 5月 232022 5月 27

出版物シリーズ

名前Proceedings - IEEE International Conference on Robotics and Automation
ISSN(印刷版)1050-4729

Conference

Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
国/地域United States
CityPhiladelphia
Period22/5/2322/5/27

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人工知能
  • 電子工学および電気工学

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