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JRM Vol.30 No.3 pp. 436-442
doi: 10.20965/jrm.2018.p0436
(2018)

Paper:

Development of an Accurate Video Shooting Method Using Multiple Drones Automatically Flying over Onuma Quasi-National Park

Sho Yamauchi*, Kouki Ogata**, Keiji Suzuki**, and Toshio Kawashima**

*Kitami Institute of Technology
165 Koencho, Kitami, Hokkaido 090-8507, Japan

**Future University Hakodate
116-2 Kamedanakano-cho, Hakodate, Hokkaido 041-8655, Japan

Received:
November 17, 2017
Accepted:
May 8, 2018
Published:
June 20, 2018
Keywords:
drone, quadcopter, UAV, multi-agent, SfM
Abstract
Development of an Accurate Video Shooting Method Using Multiple Drones Automatically Flying over Onuma Quasi-National Park

Created model for flight route planning

In recent years, promotional videos of mountains, seashores, and lakes have been created using drones. Shooting videos of natural landscapes with drones usually requires manual operation, and relies on the skill of the operator. However, if the intention is to shoot videos over a wide geographical area, manual operation is not sufficiently accurate. Therefore to accomplish this, and to take full advantage of the features of a drone, automatic operation is desirable. In this paper, we propose a method of safely modelling a video target and flight route. This includes planning for video shooting on the basis of a model, to realize accurate automatic video shooting of natural landscapes with drones. It was assumed that multiple drones would be operated simultaneously. Therefore, we developed an error verification method to compensate for performance differences between drones. To verify the usefulness of the method, it was used to shoot actual video images of Onuma quasi-national park in the south of Hokkaido Prefecture.

Cite this article as:
S. Yamauchi, K. Ogata, K. Suzuki, and T. Kawashima, “Development of an Accurate Video Shooting Method Using Multiple Drones Automatically Flying over Onuma Quasi-National Park,” J. Robot. Mechatron., Vol.30, No.3, pp. 436-442, 2018.
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Last updated on Oct. 17, 2018