인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Evaluating a robot’s estimated trajectory typically requires a ground truth trajectory, but establishing the necessary measurement infrastructure in large aircraft hangars is costly and operationally burdensome. Although alternative methods reduce this burden, they often impose other requirements in the environment, such as closed-loop routes, marker installation, or specific environmental geometry, limiting their applicability to aircraft maintenance, repair, and overhaul (MRO) inspection. This study proposes an indirect evaluation method for aircraft inspection robots without requiring a ground truth trajectory or modifications to the inspection environment. The proposed method detects relative spatial inconsistencies between the estimated trajectory and single-camera observations. It uses an object detection-tracking algorithm and the aircraft fuselage as a reference structure to evaluate the estimated trajectory by comparing it with camera observations in the image plane. To support reliable tracking without additional training, we construct herein a visual foundation model (VFM)-based detection-tracking pipeline with preprocessing and postprocessing. Field experiments in an actual aircraft exterior inspection environment compare the evaluation results of the original estimated trajectories with those obtained from drift-injected trajectories. The original trajectories achieve a mean trajectory-alignment F1 score of 0.9195, while alignment decreases as the trajectory portions subject to drift expand. These results support the method’s ability to detect spatial inconsistencies between estimated trajectories and camera observations without ground truth trajectories.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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