Abstract
Mobile robots and IoT devices demand real-time localization and dense reconstruction under tight compute and energy budgets. While 3D Gaussian Splatting (3DGS) enables efficient dense SLAM, dynamic objects and occlusions still degrade tracking and mapping. Existing dynamic 3DGS-SLAM often relies on heavy optical flow and per-frame segmentation, which is costly for mobile deployment and brittle under challenging illumination. We present DAGS-SLAM, a dynamic-aware 3DGS-SLAM system that maintains a spatiotemporal motion probability (MP) state per Gaussian and triggers semantics on demand via an uncertainty-aware scheduler. DAGS-SLAM fuses lightweight YOLO instance priors with geometric cues to estimate and temporally update MP, propagates MP to the front-end for dynamic-aware correspondence selection, and suppresses dynamic artifacts in the back-end via MP-guided optimization. Experiments on public dynamic RGB-D benchmarks show improved reconstruction and robust tracking while sustaining real-time throughput on a commodity GPU, demonstrating a practical speed-accuracy tradeoff with reduced semantic invocations toward mobile deployment.
Abstract (translated)
移动机器人和物联网设备在计算资源和能源有限的情况下，需要实时定位和密集重建。虽然3D高斯点阵（3D Gaussian Splatting，简称3DGS）能够实现高效的稠密SLAM（即时定位与地图构建），但是动态物体和遮挡仍然会降低跟踪和建图的质量。现有的动态3DGS-SLAM系统通常依赖于重量级的光流计算和逐帧分割，这在移动部署中成本高昂且在照明条件恶劣的情况下稳定性较差。 我们提出了一个名为DAGS-SLAM（动态感知3DGS-SLAM）的系统，该系统为每个高斯点维护一个时空运动概率（Motion Probability, MP）状态，并通过不确定性感知调度器按需触发语义处理。DAGS-SLAM将轻量级YOLO实例先验与几何线索相结合以估计和临时更新MP，在前端传播MP以便动态感知对应选择，同时在后端利用MP引导优化来抑制动态伪影。 实验显示，DAGS-SLAM在公共的动态RGB-D基准测试中显著提升了重建质量和跟踪鲁棒性，并能在商品级GPU上维持实时吞吐量。这表明通过减少语义调用，DAGS-SLAM实现了移动部署所需的实用速度精度权衡。
URL
https://arxiv.org/abs/2602.21644
