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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 797 - 806 (10page)
- DOI
- 10.5302/J.ICROS.2026.26.0064
이용수
초록· 키워드
LiDAR-Inertial odometry (LIO) is affected by outliers arising from data-association errors, dynamic objects, and unstable feature extraction that can degrade estimation performance and increase long-term drifts. In point-to-plane registration LIO such as FAST-LIO2, outlier handling is often implemented by applying a single global threshold to a point-to-plane distance. This approach has two key limitations: (i) it does not account for the fact that measurement uncertainty varies with the prior estimate and environment, and (ii) it treats the reliability of local plane estimation as spatially uniform within a scan, despite strong local variations. To address these limitations, we propose an adaptive outlier rejection method developed based on two complementary mechanisms. First, we employ innovation gating for uncertainty-aware outlier rejection. Specifically, measurement residuals are normalized by the innovation covariance. Second, we propose density-aware measurement classification, which uses local point density as a measure of plane-estimation reliability. The density-based classification applies class-wise thresholds to reflect heterogeneous geometric conditions within a scan. Experiments on both simulation and open datasets demonstrate that the proposed method reduces errors in pose estimates compared with the conventional approach.
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목차
- Abstract
- I. 서론
- II. FAST-LIO2 요약
- III. 이웃 점 밀도에 따른 적응형 이상치 제거
- IV. 성능 평가
- V. 결론
- REFERENCES
참고문헌
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