  According to experts, Simultaneous Localization and Mapping (SLAM) is an
intrinsic part of autonomous robotic systems. Several SLAM systems with
impressive performance have been invented and used during the last several
decades. However, there are still unresolved issues, such as how to deal with
moving objects in dynamic situations. Classic SLAM systems depend on the
assumption of a static environment, which becomes unworkable in highly dynamic
situations. Several methods have been presented to tackle this issue in recent
years, but each has its limitations. This research combines the visual SLAM
systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs
depth information and semantic segmentation to identify and eradicate dynamic
spots to accomplish semantic SLAM for dynamic situations. Evaluation of public
TUM datasets indicates that Det-SLAM is more resilient than previous dynamic
SLAM systems and can lower the estimated error of camera posture in dynamic
indoor scenarios.
