지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.11
- 수록면
- 794 - 800 (7page)
- DOI
- 10.5302/J.ICROS.2021.21.0127
이용수
초록· 키워드
오류제보하기
In this paper, we propose a new method that classifies the features in a visual-inertial navigation system. The far features provide inaccurate information on the position and velocity of a moving vehicle, and thus, the information provided by the far features should be used for attitude estimation without recovering the feature depths. In contrast, the information obtained from the near features can be used to obtain a reliable depth estimate because of the large parallax in the image plane. However, the criterion for labeling the features as near or far features is ambiguous. Previously, various geometric classification methods based on a stereo camera and measurement uncertainties have been reported. Herein, we present a criterion based on the geometric method using the MSCKF (Multi-State Constraint Kalman filter ). Additionally, we define a new concept — depth uncertainty — as the criterion of feature classification in the MSCKF. Using this criterion, we can draw a limited range defined as the observable region. Implementation of this method can decrease the error caused by the low parallax of the feature. The proposed method is validated through simulations and experiments, showing a 12.7 % and 21.2 % decrease in the mean position error, respectively, using the far features classification.
목차
Abstract
Ⅰ. 서론
Ⅱ. 시스템 및 측정 모델
Ⅲ. 시뮬레이션 및 실험
Ⅳ. 결론
REFERENCES
참고문헌 (14)
참고문헌 신청
D. Scaramuzza and F. Fraundorfer, “Visual odometry [Tutorial],” no. December, 2011.
F. Fraundorfer and D. Scaramuzza, “Visual odometry: Part II: Matching, robustness, optimization, and applications,” IEEE Robotics and Automation Magazine, vol. 19, no. 2, pp. 78-90, 2012.
J. Gui, D. Gu, S. Wang, and H. Hu, “A review of visual inertial odometry from filtering and optimisation perspectives,” Advanced Robotics, vol. 29, no. 20, pp. 1289-1301, 2015.
G. Huang, “Visual-inertial navigation : A concise review,” International Conference on Robotics and Automation, 2019.
S. Heo, J. H. Jung, and C. G. Park, “Consistent EKF-Based visual-inertial navigation using points and lines,” IEEE Sensors Journal, vol. 18, no. 18, pp. 7638-7649, 2018.
F. Fraundorfer and D. Scaramuzza, “Visual odometry: Part II: Matching, robustness, optimization, and applications,” IEEE Robotics and Automation Magazine, vol. 19, no. 2, pp. 78-90, 2012.
J. Gui, D. Gu, S. Wang, and H. Hu, “A review of visual inertial odometry from filtering and optimisation perspectives,” Advanced Robotics, vol. 29, no. 20, pp. 1289-1301, 2015.
G. Huang, “Visual-inertial navigation : A concise review,” International Conference on Robotics and Automation, 2019.
S. Heo, J. H. Jung, and C. G. Park, “Consistent EKF-Based visual-inertial navigation using points and lines,” IEEE Sensors Journal, vol. 18, no. 18, pp. 7638-7649, 2018.
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