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JRM Vol.33 No.2 pp. 292-300
doi: 10.20965/jrm.2021.p0292
(2021)

Paper:

Landing Site Detection for UAVs Based on CNNs Classification and Optical Flow from Monocular Camera Images

Chihiro Kikumoto*, Yoh Harimoto*, Kazuki Isogaya*, Takeshi Yoshida**, and Takateru Urakubo*

*Kobe University
1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan

**Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

Received:
October 14, 2020
Accepted:
February 2, 2021
Published:
April 20, 2021
Keywords:
unmanned aerial vehicle, autonomous landing, land cover classification, topographic mapping
Abstract
Landing Site Detection for UAVs Based on CNNs Classification and Optical Flow from Monocular Camera Images

Proposed method for landing site detection

The increased use of UAVs (Unmanned Aerial Vehicles) has heightened demands for an automated landing system intended for a variety of tasks and emergency landings. A key challenge of this system is finding a safe landing site in an unknown environment using on-board sensors. This paper proposes a method to generate a heat map for safety evaluation using images from a single on-board camera. The proposed method consists of the classification of ground surface by CNNs (Convolutional Neural Networks) and the estimation of surface flatness from optical flow. We present the results of applying this method to a video obtained from an on-board camera and discuss ways of improving the method.

Cite this article as:
Chihiro Kikumoto, Yoh Harimoto, Kazuki Isogaya, Takeshi Yoshida, and Takateru Urakubo, “Landing Site Detection for UAVs Based on CNNs Classification and Optical Flow from Monocular Camera Images,” J. Robot. Mechatron., Vol.33, No.2, pp. 292-300, 2021.
Data files:
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Last updated on May. 10, 2021