面向室内建筑环境机器人场景理解的具有不确定性对齐的开放词汇语义分割
Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments
AI总结：
针对辅助机器人闭集词汇检测难以理解人类指令且忽略场景不确定性导致成功率低的问题，提出利用VLMs和LLMs的开放词汇场景语义分割与检测流程，遵循“分割-检测-选择”框架实现自适应直观导航。
摘要：
全球身体残障人士数量的增加，部分归因于创伤后存活率和寿命的提高，这加剧了对先进辅助技术以改善移动性和独立性的需求。自主辅助机器人（如智能轮椅）需要强大的空间分割和语义识别能力，才能在复杂的建筑环境中有效导航。场所分割涉及划分房间或功能区域等空间区域，而语义识别则为这些区域分配语义标签，从而实现对用户特定需求的准确定位。现有方法通常利用深度学习；然而，这些闭集词汇检测系统难以理解直观和随意的人类指令。此外，大多数现有方法忽略了场景识别问题的不确定性，导致成功率较低，特别是在模糊和复杂的环境中。为了应对这些挑战，我们提出了一种开放词汇场景语义分割与检测流程，利用视觉语言模型（VLMs）和大型语言模型（LLMs）。我们的方法遵循“分割-检测-选择”框架进行开放词汇场景分类，使辅助机器人能够在建筑环境中实现自适应和直观的导航。
英文摘要：
The global rise in the number of people with physical disabilities, in part due to improvements in post-trauma survivorship and longevity, has amplified the demand for advanced assistive technologies to improve mobility and independence. Autonomous assistive robots, such as smart wheelchairs, require robust capabilities in spatial segmentation and semantic recognition to navigate complex built environments effectively. Place segmentation involves delineating spatial regions like rooms or functional areas, while semantic recognition assigns semantic labels to these regions, enabling accurate localization to user-specific needs. Existing approaches often utilize deep learning; however, these close-vocabulary detection systems struggle to interpret intuitive and casual human instructions. Additionally, most existing methods ignore the uncertainty of the scene recognition problem, leading to low success rates, particularly in ambiguous and complex environments. To address these challenges, we propose an open-vocabulary scene semantic segmentation and detection pipeline leveraging Vision Language Models (VLMs) and Large Language Models (LLMs). Our approach follows a 'Segment Detect Select' framework for open-vocabulary scene classification, enabling adaptive and intuitive navigation for assistive robots in built environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
