Quadcopter Tracks Quadcopter via Real-Time Shape Fitting

Dror Epstein; Dan Feldman

doi:10.1109/LRA.2017.2773668

Quadcopter Tracks Quadcopter via Real-Time Shape Fitting

Dror Epstein, Dan Feldman

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

We suggest a novel algorithm that tracks given shapes in real time from a low-quality video stream. The algorithm is based on a careful selection of a small subset of pixels that suffices to obtain an approximation of the observed shape. The shape can then be extracted quickly from the small subset. We implemented the algorithm in a system for mutual localization of a group of low-cost toy-quadcopters. Each quadcopter carries only a single 8-g RGB camera, and stabilizes itself via real-time tracking of the other quadcopters in ∼ 30 frames/s. Existing algorithms for real-time shape fitting are based on more expensive hardware, external cameras, or have significantly worse performance. We provide full open source to our algorithm, experimental results, benchmarks, and video that demonstrates our system. We then discuss generalizations to other shapes and extensions for more robotics applications.

Original language	English
Article number	8110663
Pages (from-to)	544-550
Number of pages	7
Journal	IEEE Robotics and Automation Letters
Volume	3
Issue number	1
DOIs	https://doi.org/10.1109/LRA.2017.2773668
State	Published - Jan 2018

Bibliographical note

Funding Information:
This work was supported in part by The Rothschild Caesaria Foundation under Grant 24196615 and in part by the German-Israel Foundation under Grant 2048.

Funding Information:
Manuscript received June 17, 2017; accepted October 21, 2017. Date of publication November 15, 2017; date of current version December 11, 2017. This paper was recommended for publication by Associate Editor G. Nejat and Editor J. Wen upon evaluation of the reviewers’ comments. This work was supported in part by The Rothschild Caesaria Foundation under Grant 24196615 and in part by the German-Israel Foundation under Grant 2048. (Corresponding author: Dror Epstein.) The authors are with the Robotic and Big Data Lab, Computer Science Department, University of Haifa, Haifa 3498838, Israel (e-mail: dror.epstein@ gmail.com; [email protected]). Digital Object Identifier 10.1109/LRA.2017.2773668

Publisher Copyright:
© 2017 IEEE.

Keywords

Autonomous vehicle navigation
RGB-D perception

ASJC Scopus subject areas

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

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10.1109/LRA.2017.2773668

Cite this

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abstract = "We suggest a novel algorithm that tracks given shapes in real time from a low-quality video stream. The algorithm is based on a careful selection of a small subset of pixels that suffices to obtain an approximation of the observed shape. The shape can then be extracted quickly from the small subset. We implemented the algorithm in a system for mutual localization of a group of low-cost toy-quadcopters. Each quadcopter carries only a single 8-g RGB camera, and stabilizes itself via real-time tracking of the other quadcopters in ∼ 30 frames/s. Existing algorithms for real-time shape fitting are based on more expensive hardware, external cameras, or have significantly worse performance. We provide full open source to our algorithm, experimental results, benchmarks, and video that demonstrates our system. We then discuss generalizations to other shapes and extensions for more robotics applications.",

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Quadcopter Tracks Quadcopter via Real-Time Shape Fitting

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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