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 paper proposes a real-time localization-recovery method for autonomous mobile robots based on LiDAR-map structural mismatch detection and adaptive adjustment of the Adaptive Monte Carlo Localization (AMCL) pose covariance. The proposed method detects slip-induced localization inconsistencies from structural discrepancies between the map and real-time LiDAR observations while preserving the original AMCL motion and measurement models. When a localization inconsistency is detected, the pose covariance is expanded according to the average corner-angle variation, thereby increasing the particle search region. Subsequent LiDAR-based measurement updates and resampling enable the AMCL pose estimate to reconverge. Because the original AMCL localization framework remains unchanged, the proposed method can be integrated with the existing ROS Navigation Stack as an independent module. The method was evaluated under three representative scenarios: a low-friction floor, an unsecured rug, and an external impact. The effects of the proposed method were evaluated separately using the actual robot trajectories measured by a ceiling-camera system and the AMCL-estimated trajectories obtained from the amcl_pose topic. In all three scenarios, the final-pose errors relative to the navigation goal decreased after applying the proposed method, as evaluated separately using the actual and AMCL-estimated poses. These results indicate that the proposed method educes final-pose deviations from the Navigation Goal under slip-induced localization inconsistencies and improves the robustness of AMCL-based navigation without requiring additional onboard sensors beyond the wheel encoder and LiDAR already used by AMCL.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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