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
- 학위논문
- 저자정보
- 지도교수
- 이현재
- 발행연도
- 2017
- 저작권
- 조선대학교 논문은 저작권에 의해 보호받습니다.
이용수5
이 논문의 연구 히스토리 (4)
2018
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초록· 키워드
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This thesis presents vision-based relative position and attitude estimation system.
Image processing algorithm have been proposed to apply relative navigation algorithm.
First ltering is performed through the preprocessing and labeling process and object
recognition and detection algorithms are performed on the selected objects
Target object recognition and identication determine the success of the mission.
Several operating environment features are used to ensure accuracy of target object
selection. First, we reduced the image noise using the geometric characteristics of the
target object. The target object takes the specied geometry. Second, we use object
tracking to improve system performance. The image processing process takes the most
time in the entire system. When the target object is selected, use the object tracking
algorithm to reduce the overall system turnaround time. This process imporves the
reliability of the entire system by reducing the dierence between image frames.
The relative position and attitude estimation algorithms introduce Gaussian Least
Squares Dierential Correction(GLSDC) and Newton''s Levenberg-Marquadt(LM) and
are generally known as the Gauss Newton method. GLSDC guarantees convergence
when the starting point of the selected initial value approaches the targer value. The
LM algorithm can reduce the divergence of GLSDC.
The vision-based estimation algorithm estimates the relative position and attitude
information in the object space based on th distance between the object information
on the image plan and the known target object. The algorithms introduced use ground
tests to analyze the feasibility.
Image processing algorithm have been proposed to apply relative navigation algorithm.
First ltering is performed through the preprocessing and labeling process and object
recognition and detection algorithms are performed on the selected objects
Target object recognition and identication determine the success of the mission.
Several operating environment features are used to ensure accuracy of target object
selection. First, we reduced the image noise using the geometric characteristics of the
target object. The target object takes the specied geometry. Second, we use object
tracking to improve system performance. The image processing process takes the most
time in the entire system. When the target object is selected, use the object tracking
algorithm to reduce the overall system turnaround time. This process imporves the
reliability of the entire system by reducing the dierence between image frames.
The relative position and attitude estimation algorithms introduce Gaussian Least
Squares Dierential Correction(GLSDC) and Newton''s Levenberg-Marquadt(LM) and
are generally known as the Gauss Newton method. GLSDC guarantees convergence
when the starting point of the selected initial value approaches the targer value. The
LM algorithm can reduce the divergence of GLSDC.
The vision-based estimation algorithm estimates the relative position and attitude
information in the object space based on th distance between the object information
on the image plan and the known target object. The algorithms introduced use ground
tests to analyze the feasibility.
목차
1.서론 12.영상처리 32.1 영상 전처리 52.2 영상 매칭 72.3 객체 추적 163. 상대위치 추정 203.1 Gaussian Least Squares Differential Correlation(GLSDC) 223.2 Levenberg-Marquardt(LM) 254. 시뮬레이션 및 지상시험 274.1 영상처리 시뮬레이션 274.2 상대항법 시뮬레이션 334.3 지상시험 385. 결론 43[참고문헌] 44
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