FrontierGS: Exploration-Driven Active Gaussian Reconstruction with Efficient Fisher-Based Planning

Active Gaussian reconstruction requires robots to explore unknown space while improving the reconstruction quality of observed surfaces. Existing methods may prioritize local reconstruction gains during viewpoint planning, potentially trapping robots in observed regions. Repeated Gaussian map optimization and viewpoint evaluation also require substantial computation. To address these problems, we present FrontierGS, an exploration-driven framework that organizes viewpoint planning around a main path toward unknown space and adopts Fisher information to estimate the geometric benefit of candidate viewpoints. For efficient estimation of reconstruction gains, we reuse the Fisher information computed from past observations, forming a persistent Fisher approximation that avoids recomputing historical information after map updates. To guide viewpoint selection, we accumulate the expected Fisher information of already selected viewpoints to predict which viewpoints near the main path can provide complementary reconstruction benefits. These predictions use previously computed Fisher information between map optimizations, which lead to accumulated prediction errors. We therefore introduce an adaptive refresh strategy that optimizes the map and recomputes candidate Fisher information. Experiments demonstrate that our method improves coverage and rendering quality in large scenes while reducing computational cost.