MCOO-SLAM：一种多相机全向物体SLAM系统
MCOO-SLAM: A Multi-Camera Omnidirectional Object SLAM System
AI总结：
MCOO-SLAM利用多相机全向配置，结合语义-几何-时间融合与全向回环，实现复杂室外场景下稳健的物体级SLAM，提升定位精度和地图一致性。
摘要：
物体级SLAM提供结构化和语义丰富的环境表示，使其更具可解释性，并适用于高级机器人任务。然而，大多数现有方法依赖RGB-D传感器或单目视图，这些方法受限于狭窄的视野、对遮挡敏感以及深度感知有限，尤其是在大规模或室外环境中。这些限制通常使系统只能从有限视角观察物体的部分视图，导致物体建模不准确和数据关联不可靠。在本工作中，我们提出MCOO-SLAM，一种新颖的多相机全向物体SLAM系统，充分利用环视相机配置，在复杂室外场景中实现稳健、一致且语义增强的地图构建。我们的方法整合了点特征和带有开放词汇语义的物体级地标。引入了一种语义-几何-时间融合策略，用于跨多个视图的稳健物体关联，从而提高一致性并实现精确的物体建模，并设计了一个全向回环闭合模块，利用场景级描述符实现视点不变的场所识别。此外，构建的地图被抽象为分层3D场景图，以支持下游推理任务。在真实世界中的大量实验表明，MCOO-SLAM实现了精确的定位和可扩展的物体级地图构建，并提高了对遮挡、姿态变化和环境复杂性的鲁棒性。
英文摘要：
Object-level SLAM offers structured and semantically meaningful environment representations, making it more interpretable and suitable for high-level robotic tasks. However, most existing approaches rely on RGB-D sensors or monocular views, which suffer from narrow fields of view, occlusion sensitivity, and limited depth perception-especially in large-scale or outdoor environments. These limitations often restrict the system to observing only partial views of objects from limited perspectives, leading to inaccurate object modeling and unreliable data association. In this work, we propose MCOO-SLAM, a novel Multi-Camera Omnidirectional Object SLAM system that fully leverages surround-view camera configurations to achieve robust, consistent, and semantically enriched mapping in complex outdoor scenarios. Our approach integrates point features and object-level landmarks enhanced with open-vocabulary semantics. A semantic-geometric-temporal fusion strategy is introduced for robust object association across multiple views, leading to improved consistency and accurate object modeling, and an omnidirectional loop closure module is designed to enable viewpoint-invariant place recognition using scene-level descriptors. Furthermore, the constructed map is abstracted into a hierarchical 3D scene graph to support downstream reasoning tasks. Extensive experiments in real-world demonstrate that MCOO-SLAM achieves accurate localization and scalable object-level mapping with improved robustness to occlusion, pose variation, and environmental complexity.
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。
