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

TL;DR

DAGS-SLAM introduces a dynamic-aware 3D Gaussian Splatting SLAM system that efficiently handles dynamic objects using spatiotemporal motion probabilities and an uncertainty-aware scheduler, improving real-time dense mapping on mobile devices.

Contribution

The paper proposes a novel dynamic-aware 3DGS-SLAM method that reduces reliance on heavy optical flow and segmentation, enabling real-time dense SLAM on mobile hardware.

Findings

Improved reconstruction accuracy in dynamic environments.

Robust tracking performance demonstrated on public benchmarks.

Achieves real-time processing on commodity GPUs.

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.