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
This study proposes an offline processing method to address discontinuous drone odometry and improve mapping accuracy in terrestrial mapping applications using single-channel LiDAR. Odometry discontinuities frequently occur during drone-based data logging due to hardware constraints such as on- board computer overheating, communication instability, and processing delays. To overcome these limitations, this study extracts ORB-SLAM3-based visual odometry from logged camera images and uses a conditional complement strategy to selectively replace drone odometry only in discontinuous sections, ensuring continuity while maintaining accuracy. Furthermore, we construct individual maps for each flight path and apply ICP-based map-to-map matching to compensate for z-value errors due to the vertical accuracy limitations of RTK. Experimental results demonstrate that the proposed approach significantly improves continuity while maintaining accuracy comparable to drone odometry. Quantitative evaluation results demonstrate that ICP matching significantly reduces z-value errors across all experimental regions. This offline processing method produces maps with higher density than conventional methods while maintaining accuracy, effectively providing reliable topographic information despite the limitations of single-channel LiDAR systems. The results of this study will contribute to the development of practical high-precision mapping systems applicable to various fields, including agriculture and construction, in resource-constrained environments.
본문·목차
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UCI(KEPA) : I410-151-26-02-095562006
