Abstract
This paper describes a Simultaneous Localization And Mapping (SLAM) algorithm using a monocular camera for a small Unmanned Aerial Vehicle (UAV). A small UAV has attracted the attention for effective means of the collecting aerial information. However, there are few practical applications due to its small payloads for the 3D measurement. We propose extended Kalman filter SLAM to increase UAV position and attitude data and to construct 3D terrain maps using a small monocular camera. We propose 3D measurement based on Scale-Invariant Feature Transform (SIFT) triangulation features extracted from captured images. Field-experiment results show that our proposal effectively estimates position and attitude of the UAV and construct the 3D terrain map.
| Original language | English |
|---|---|
| Pages (from-to) | 292-301 |
| Number of pages | 10 |
| Journal | Journal of Robotics and Mechatronics |
| Volume | 23 |
| Issue number | 2 |
| Publication status | Published - 2011 Apr |
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Keywords
- 3D reconstruction
- SIFT
- SLAM
- UAV
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science(all)
Cite this
3D terrain reconstruction by small Unmanned Aerial Vehicle using SIFT-based monocular SLAM. / Suzuki, Taro; Amano, Yoshiharu; Hashizume, Takumi; Suzuki, Shinji.
In: Journal of Robotics and Mechatronics, Vol. 23, No. 2, 04.2011, p. 292-301.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - 3D terrain reconstruction by small Unmanned Aerial Vehicle using SIFT-based monocular SLAM
AU - Suzuki, Taro
AU - Amano, Yoshiharu
AU - Hashizume, Takumi
AU - Suzuki, Shinji
PY - 2011/4
Y1 - 2011/4
N2 - This paper describes a Simultaneous Localization And Mapping (SLAM) algorithm using a monocular camera for a small Unmanned Aerial Vehicle (UAV). A small UAV has attracted the attention for effective means of the collecting aerial information. However, there are few practical applications due to its small payloads for the 3D measurement. We propose extended Kalman filter SLAM to increase UAV position and attitude data and to construct 3D terrain maps using a small monocular camera. We propose 3D measurement based on Scale-Invariant Feature Transform (SIFT) triangulation features extracted from captured images. Field-experiment results show that our proposal effectively estimates position and attitude of the UAV and construct the 3D terrain map.
AB - This paper describes a Simultaneous Localization And Mapping (SLAM) algorithm using a monocular camera for a small Unmanned Aerial Vehicle (UAV). A small UAV has attracted the attention for effective means of the collecting aerial information. However, there are few practical applications due to its small payloads for the 3D measurement. We propose extended Kalman filter SLAM to increase UAV position and attitude data and to construct 3D terrain maps using a small monocular camera. We propose 3D measurement based on Scale-Invariant Feature Transform (SIFT) triangulation features extracted from captured images. Field-experiment results show that our proposal effectively estimates position and attitude of the UAV and construct the 3D terrain map.
KW - 3D reconstruction
KW - SIFT
KW - SLAM
KW - UAV
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UR - http://www.scopus.com/inward/citedby.url?scp=84863011625&partnerID=8YFLogxK
M3 - Article
VL - 23
SP - 292
EP - 301
JO - Journal of Robotics and Mechatronics
JF - Journal of Robotics and Mechatronics
SN - 0915-3942
IS - 2
ER -