DAGS-SLAM: Dynamic-Aware 3DGS SLAM via Spatiotemporal Motion Probability and Uncertainty-Aware Scheduling

TL;DR

DAGS-SLAM is presented, a dynamic-aware 3DGS-SLAM system that maintains a spatiotemporal motion probability state per Gaussian and triggers semantics on demand via an uncertainty-aware scheduler, demonstrating a practical speed-accuracy tradeoff with reduced semantic invocations toward mobile deployment.

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.

Figures (9)

• Figure 1: Typical failure cases of 3DGS-SLAM in dynamic scenes. Moving objects are often fused into the Gaussian map, leading to ghosting/blobs, smeared textures, and broken surfaces, which in turn degrades both tracking stability and static-scene reconstruction quality.
• Figure 2: System pipeline of DAGS-SLAM. A semantic thread maintains a temporally updated per-Gaussian MP state by fusing lightweight instance priors with geometric cues, and an uncertainty-aware scheduler triggers semantics on demand to reduce overhead. MP is propagated to the tracking thread for dynamic-aware correspondence filtering and to the mapping thread for MP-guided rendering optimization, producing clean static-scene reconstructions under dynamic interference.
• Figure 3: Illustration of the proposed labeling process, where yellow denotes dynamic outliers and green denotes static features.
• Figure 4: The uncertainty-aware semantic-on-demand scheduling mechanism. MP uncertainty and tracking/geometry inconsistency are fused into a trigger score to decide whether to refresh the instance prior. Segmentation is invoked only at trigger moments; otherwise the prior is reused and MP is updated temporally.
• Figure 5: Epipolar-verified static recovery and densification. We first obtain a coarse MP-based dynamic mask to localize ambiguous boundary regions, then use epipolar geometry to validate static correspondences and filter dynamic outliers. The verified static regions are restored and incorporated into the densification stage.