TY - JOUR
T1 - Quadcopter Tracks Quadcopter via Real-Time Shape Fitting
AU - Epstein, Dror
AU - Feldman, Dan
N1 - 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.
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - Autonomous vehicle navigation
KW - RGB-D perception
UR - http://www.scopus.com/inward/record.url?scp=85063308381&partnerID=8YFLogxK
U2 - 10.1109/LRA.2017.2773668
DO - 10.1109/LRA.2017.2773668
M3 - Article
SN - 2377-3766
VL - 3
SP - 544
EP - 550
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 1
M1 - 8110663
ER -