SGA-SLAM: A Semantic-Geometric Adaptive RGB-D SLAM System for Dynamic Environments
To address pose estimation drift and dynamic artifacts in dense mapping caused by moving-object interference in visual SLAM, this paper proposes SGA-SLAM, a robust RGB-D SLAM system tailored for complex indoor dynamic environments. The system is built upon the ORB-SLAM3 framework and incorporates the lightweight instance segmentation model YOLO11n-seg to obtain semantic priors of dynamic objects and perform preliminary feature screening within potentially dynamic regions. Furthermore, a cascaded geometric screening mechanism that integrates epipolar geometric constraints with adaptive depth consistency verification is designed to further improve the accuracy of dynamic feature discrimination, thereby effectively removing dynamic features while retaining stable static features. Experimental results demonstrate that SGA-SLAM substantially reduces the absolute trajectory error in highly dynamic indoor scenes from the TUM RGB-D and Bonn RGB-D Dynamic datasets. Specifically, the ATE RMSE is reduced by more than 94% across all TUM RGB-D Walking sequences, and the proposed method achieves higher localization accuracy with lower trajectory-error dispersion on most Bonn RGB-D Dynamic sequences. Moreover, the proposed method effectively suppresses dynamic artifacts and point-cloud contamination in dense mapping, generating static environment maps with clearer structures and improved geometric consistency.