Lightweight SLAM with a hierarchical dynamic-object discrimination mechanism

Purpose This study aims to address the degradation of localisation and mapping accuracy in visual simultaneous location and mapping (SLAM) caused by moving objects in dynamic environments. This study proposes a lightweight SLAM method based on a hierarchical dynamic-object discrimination mechanism. Design/methodology/approach A three-layer pipeline is established: a pruned and quantised YOLOv8n-seg performs real-time instance segmentation to remove features of inherently dynamic categories; dense scene flow combined with rigid-motion consistency and statistical criteria identifies potentially dynamic objects among semantically static ones; during back-end optimisation, dynamic observation factors are decoupled, while a progressive feature management strategy maximises the retention of static constraints. Findings Evaluations on highly dynamic TUM RGB-D sequences (fr3\_walking\_xyz, fr3\_walking\_half) demonstrate that the proposed method outperforms ORB-SLAM3, DynaSLAM and DS-SLAM, achieving ATE/RPE of 0.014 / 0.017 m and 0.019/0.023 m, respectively, confirming its robustness, accuracy and real-time performance. Originality/value This study proposes a novel lightweight hierarchical framework that integrates efficient semantic segmentation, motion-aware geometric discrimination and dynamic-factor decoupling in optimisation. It offers a practical and accurate solution for robust visual SLAM in real-world dynamic environments.