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.

翻译：绝大多数视觉SLAM系统在动态场景中缺乏鲁棒性。现有处理场景中动态物体的方法通常依赖基于深度学习的检测与过滤技术，但这些方法无法应对未知运动物体。本文提出开源视觉SLAM系统Panoptic-SLAM，该系统对动态环境具有鲁棒性，即使存在未知物体也能稳定运行。该方法在状态估计过程中利用全景分割过滤场景中的动态物体。Panoptic-SLAM基于ORB-SLAM3（面向静态环境的先进SLAM系统）开发，采用真实世界数据集进行测试，并与文献中多个先进系统（包括DynaSLAM、DS-SLAM、SaD-SLAM、PVO和FusingPanoptic）进行对比。例如，Panoptic-SLAM的平均定位精度比当前最新的基于全景分割的视觉SLAM方法PVO高四倍。此外，研究还使用搭载RGB-D相机的四足机器人进行了真实场景应用实验，并通过运动捕捉系统生成的地面真值数据验证了测试结果。