GridClust-SLAM: robust SLAM in dynamic environments via grid-based clustering and semantic filtering

Abstract Despite the significant progress in dynamic SLAM research, achieving an optimal balance among accuracy, efficiency, and robustness remains an open challenge. To address this issue, this paper proposes GridClust-SLAM, an RGB-D SLAM method that integrates grid-based analysis with a lightweight semantic guidance mechanism. The method partitions the image into a grid, utilizing each grid cell as the fundamental unit for analysis. An edge-aware grid density clustering module is designed: initially, edge information is leveraged to isolate grid cells containing object boundaries; subsequently, the remaining non-edge grid cells are subjected to a weighted DBSCAN clustering based on their multi-dimensional attributes to obtain the grid clustering results. Furthermore, under the semantic guidance provided by a lightweight network, these clustering results are filtered and expanded in conjunction with a depth consistency check to accurately generate a dynamic grid mask. Experimental results on the TUM, BONN, and ICL-NUIM datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in terms of both absolute trajectory error and relative pose error, while maintaining high computational efficiency.