Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
This work addresses the challenge of dynamic environments for autonomous robotic systems, but it is incremental as it builds on existing SLAM and segmentation methods.
The paper tackles the problem of moving objects in dynamic scenes for visual SLAM by combining ORB-SLAM3 and Detectron2 to identify and remove dynamic spots using depth and semantic segmentation, resulting in lower camera pose error and greater resilience compared to previous systems on the TUM datasets.
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.