ADA-DPM: A Neural Descriptors-based Adaptive Noise Point Filtering Strategy for SLAM

Abstract: LiDAR SLAM has demonstrated significant application value in various fields, including mobile robot navigation and high-precision map construction. However, existing methods often need to make a trade-off between positioning accuracy and system robustness when faced with dynamic object interference, point cloud noise, and unstructured environments. To address this challenge, we propose an adaptive noise filtering SLAM strategy-ADA-DPM, achieving excellent preference in both aspects. We design the Dynamic Segmentation Head to predict the category of feature points belonging to dynamic points, to eliminate dynamic feature points; design the Global Importance Scoring Head to adaptively select feature points with higher contribution and features while suppressing noise interference; and construct the Cross Layer Intra-Graph Convolution Module (GLI-GCN) to fuse multi-scale neighborhood structures, thereby enhancing the discriminative ability of overlapping features. Finally, to further validate the effectiveness of our method, we tested it on several publicly available datasets and achieved outstanding results.