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.
초록·키워드
Visual Place Recognition (VPR) systems typically exhibit reduced robustness when subjected to changes in scene appearance produced by illumination dynamics or heterogeneity across different types of visual sensors. This paper proposes a novel framework that exploits depth estimation techniques to overcome these challenges. Our approach transforms omnidirectional images into depth maps using Distill Any Depth, a state-of-the-art depth estimator based on Depth Anything V2. These depth maps are then converted into pseudo-LiDAR point clouds, which serve as input to the MinkUNeXt architecture, which generates global-appearance descriptors. A key innovation lies in our novel data augmentation technique that exploits different distilled variants of depth estimation models to enhance robustness across varying conditions. Despite training with a limited set of images captured only under cloudy conditions, our system demonstrates robust performance when evaluated across diverse lighting scenarios, and further tests with different datasets and camera types confirm its generalization to geometrically dissimilar inputs. Extensive comparisons with state-of-the-art methods prove that our approach performs competitively across diverse lighting conditions, particularly excelling in scenarios with significant illumination changes. Furthermore, the generation of pseudo-LiDAR information from standard cameras provides a cost-effective alternative to 3D sensors. In summary, this work presents a fundamentally different approach to scene representation for VPR, with promising implications for robot localization in challenging environments. The implementation is publicly available at https://juanjo-cabrera.github.io/projects-pL-MinkUNeXt/ .
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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