To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning.

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments.