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
- 743 - 752 (10page)
- DOI
- 10.5302/J.ICROS.2026.26.0023
이용수
초록· 키워드
This study presents a self-supervised traversability estimation algorithm and an integrated navigation system for unmanned ground vehicles (UGVs) operating in unstructured off-road environments without the use of pre-built maps. Conventional approaches, such as model-predictive-control based traversability modeling and LiDAR-based obstacle detection, often suffer from high computational cost, sensitivity to parameter tuning, and inaccurate terrain classification. RGB-based semantic segmentation alone is also vulnerable to illumination changes and color ambiguities, leading to unreliable performance in unstructured terrains. To address these challenges, the proposed method fuses RGB-d sensory data, semantic features, and real-world driving experience within a self-supervised learning framework. Depth SLIC- based superpixel segmentation reduces dependence on RGB appearance and provides geometrically consistent regions, while superpixel-level aggregation of semantic probabilities creates compact and discriminative feature vectors. Traversability labels are automatically generated from driving data by analyzing discrepancies between commanded and actual velocities, and roll and pitch variations, and are projected onto the image plane for dense supervision without manual annotation. An multilayer-perceptron based model predicts traversability while maintaining consistency among semantic, geometric, and motion features. The predicted traversability is transformed into a bird's-eye-view local cost map and integrated with the ROS2 NAV2 DWB controller, enabling real-time navigation without using a global map. Experiments on a custom off-road UGV platform demonstrate reliable obstacle avoidance and traversable region estimation, highlighting the method's effectiveness in enhancing autonomy and robustness in extreme off-road conditions.
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목차
- Abstract
- I. 서론
- II. 연구 방법
- III. 실험
- IV. 고찰
- V. 결론
- REFERENCES
참고문헌
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