Exploiting the accuracy-acceleration tradeoff: VINS-assisted real-time object detection on moving systems

Betty Le Dem, Kazuo Nakazawa

研究成果: Conference contribution

抄録

In recent years, Convolutional Neural Networks (CNNs) have repeatedly shown state-of-the-art performance for their accuracy in the task of object detection, but their heavy computational costs impede their ability for real-time detection when the supporting system is moving, particulary when it is accelerating. At the same time, recent progress on visual inertial systems takes great advantage of movement information to robustly estimate the robot state and its surrounding. This paper proposes to exploit the advantages of inertial odometry research for the purpose of real-time object detection system on mobile robots. We combine a CNN detector with VINS-Mono, a moving visual odometry system, and show reliable improvement in the detection process, especially when the robot accelerates or decelerates. Our system is ready-to-use in that it has very low deployment cost and requires no calibration. The resulting system allows for simultaneous robot state estimation and object detection, as well as object tracking. Lastly, this architecture proves to be flexible because not restrained to a specific object type or detector.

本文言語English
ホスト出版物のタイトルProceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ483-488
ページ数6
ISBN(電子版)9781728124933
DOI
出版ステータスPublished - 2019 7
イベント2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
継続期間: 2019 7 82019 7 12

出版物シリーズ

名前IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
2019-July

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
CountryChina
CityHong Kong
Period19/7/819/7/12

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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