提出轻量动态感知3DGS-SLAM，提升移动设备实时建图精度
DAGS-SLAM: Dynamic-Aware 3DGS SLAM via Spatiotemporal Motion Probability and Uncertainty-Aware Scheduling
- 用时空运动概率动态追踪每个高斯点，减少对昂贵分割的依赖
- 在公开数据集上实现更优重建质量与鲁棒跟踪，保持实时帧率
- 适合资源受限的移动端部署，减少语义调用次数以降低功耗
移动机器人和物联网设备在计算与能耗受限条件下，亟需实时定位与稠密重建。尽管3D高斯点云拼贴（3DGS）实现了高效稠密SLAM，但动态物体和遮挡仍导致跟踪与建图性能下降。现有动态3DGS-SLAM多依赖重型光流与逐帧分割，难以在移动端部署且在复杂光照下易失效。本文提出DAGS-SLAM，为每个高斯点维护时空运动概率（MP）状态，并通过不确定性感知调度器按需触发语义信息。系统融合轻量级YOLO实例先验与几何线索估计并时序更新MP，将MP传递至前端用于动态感知对应点选择，并在后端通过MP引导优化抑制动态伪影。在公开动态RGB-D基准测试上，DAGS-SLAM实现更优重建效果与鲁棒跟踪，同时在消费级GPU上维持实时吞吐，展示出面向移动端部署的实用速度-精度权衡，且显著减少语义调用次数。
原文摘要 · Abstract (English)
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.
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