Lightweight 3DGS-SLAM for Memory-Constrained Environments: Spatial-Aware Truncation and Adaptive Antihallucination Restoration Mechanism

Dense simultaneous localization and mapping (SLAM) via 3D Gaussian splatting (3DGS) faces memory bottlenecks due to the explosive growth of primitives during long-sequence mapping. We propose SATA-SLAM, a framework featuring spatial-aware truncation and adaptive anti-hallucination. The online front-end maintains a constant memory footprint via a spatial-aware pruning module (SAPM), which employs a survival scoring function that couples primitive opacity with view-frustum projection coverage and a temporal protection window. Subsequently, an anti-hallucination generative refinement module (AGRM) utilizes texture priors from pretrained diffusion models for offline inpainting of residual regions. In addition, an adaptive gating mechanism to verify and suppress AIGC-induced hallucinations caused by pose drift, ensuring multiview consistency. Experiments on the public Replica dataset show that SATA-SLAM improves rendering quality from 12.5 dB to 37.44 dB (averaged over the Replica room0 and office0 scenes) while using only 26% of the original memory, outperforming the unconstrained baseline. This study provides a pathway toward low-power, high-fidelity environmental perception for mobile robots.