  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.
