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
- 838 - 845 (8page)
- DOI
- 10.5302/J.ICROS.2026.26.0059
이용수
초록· 키워드
Accurate state estimation is essential for quadruped robots operating in environments where exteroceptive sensing—such as LiDAR or cameras—is unavailable. This makes proprioceptive odometry critical for maintaining consistent state estimation. Among the existing approaches, recent learning-based, leg-inertial odometry methods rely primarily on internal sensor observations and do not explicitly account for variations in system dynamics, such as changes in externally added masses. We propose the hierarchical ground-reaction-force-aided learning leg-inertial odometry (HG-LLIO) framework, a physics-informed proprioceptive odometry framework that explicitly incorporates payload information and ground reaction force (GRF) estimation as a physically meaningful intermediate representation. The HG-LLIO framework consists of two stages: a GRF estimation network that fuses proprioceptive measurements with payload mass and location, followed by an odometry network that predicts incremental position and orientation changes using the estimated GRF as a latent physical feature. Uncertainty is modeled based on a likelihood-based regression formulation that jointly estimates prediction mean and covariance. Simulation-based evaluations in the Isaac Gym and Gazebo environments demonstrate that explicitly accounting for payload variations improves the consistency of odometry estimation under changing load conditions. While the impact on mean error reduction is limited, introducing GRF-guided intermediate representations contributes to more stable and interpretable uncertainty behavior across dynamic scenarios. These results indicate that integrating payload awareness and physically grounded intermediate variables enhances estimation robustness and uncertainty characterization in perception-degraded environments, even in the presence of inherent accumulated drifts.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- Abstract
- I. 서론
- II. 제안하는 학습 구조
- III. 시뮬레이션 실험
- IV. 결론
- REFERENCES
참고문헌
참고문헌 신청최근 본 자료
