Taming the Light: Illumination-Invariant Semantic 3DGS-SLAM
This addresses robustness issues in tightly-coupled semantic SLAM systems for applications like robotics or AR/VR, though it is incremental as it builds on existing 3DGS-SLAM methods.
The paper tackles the problem of extreme exposure degrading 3D map reconstruction and semantic segmentation in SLAM systems, achieving state-of-the-art performance in camera tracking, map quality, and semantic and geometric accuracy on public datasets.
Extreme exposure degrades both the 3D map reconstruction and semantic segmentation accuracy, which is particularly detrimental to tightly-coupled systems. To achieve illumination invariance, we propose a novel semantic SLAM framework with two designs. First, the Intrinsic Appearance Normalization (IAN) module proactively disentangles the scene's intrinsic properties, such as albedo, from transient lighting. By learning a standardized, illumination-invariant appearance model, it assigns a stable and consistent color representation to each Gaussian primitive. Second, the Dynamic Radiance Balancing Loss (DRB-Loss) reactively handles frames with extreme exposure. It activates only when an image's exposure is poor, operating directly on the radiance field to guide targeted optimization. This prevents error accumulation from extreme lighting without compromising performance under normal conditions. The synergy between IAN's proactive invariance and DRB-Loss's reactive correction endows our system with unprecedented robustness. Evaluations on public datasets demonstrate state-of-the-art performance in camera tracking, map quality, and semantic and geometric accuracy.