Panoptic-SLAM：使用全景分割的动态环境视觉SLAM
Panoptic-SLAM: Visual SLAM in Dynamic Environments using Panoptic Segmentation
AI总结：
Panoptic-SLAM基于ORB-SLAM3，利用全景分割过滤动态物体，在含未知运动物体的动态环境中实现鲁棒视觉SLAM，精度显著优于PVO等现有方法，并在四足机器人上验证了实用性。
摘要：
大多数视觉SLAM系统在动态场景中不具备鲁棒性。那些处理场景中动态物体的系统通常依赖基于深度学习的方法来检测和过滤这些物体。然而，这些方法无法处理未知的运动物体。本工作提出了Panoptic-SLAM，一个对动态环境具有鲁棒性的开源视觉SLAM系统，即使在存在未知物体的情况下也能正常工作。它在状态估计过程中使用全景分割来过滤场景中的动态物体。Panoptic-SLAM基于ORB-SLAM3，这是一个面向静态环境的最先进的SLAM系统。该实现使用真实世界数据集进行了测试，并与文献中的多个最先进系统进行了比较，包括DynaSLAM、DS-SLAM、SaD-SLAM、PVO和FusingPanoptic。例如，Panoptic-SLAM的平均精度比PVO高四倍，PVO是最新的基于全景分割的视觉SLAM方法。此外，还使用搭载RGB-D相机的四足机器人进行了实验，以测试我们的方法在真实世界场景中的适用性。测试通过运动捕捉系统建立的真值进行了验证。
英文摘要：
The majority of visual SLAM systems are not robust in dynamic scenarios. The ones that deal with dynamic objects in the scenes usually rely on deep-learning-based methods to detect and filter these objects. However, these methods cannot deal with unknown moving objects. This work presents Panoptic-SLAM, an open-source visual SLAM system robust to dynamic environments, even in the presence of unknown objects. It uses panoptic segmentation to filter dynamic objects from the scene during the state estimation process. Panoptic-SLAM is based on ORB-SLAM3, a state-of-the-art SLAM system for static environments. The implementation was tested using real-world datasets and compared with several state-of-the-art systems from the literature, including DynaSLAM, DS-SLAM, SaD-SLAM, PVO and FusingPanoptic. For example, Panoptic-SLAM is on average four times more accurate than PVO, the most recent panoptic-based approach for visual SLAM. Also, experiments were performed using a quadruped robot with an RGB-D camera to test the applicability of our method in real-world scenarios. The tests were validated by a ground-truth created with a motion capture system.
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。
