Efficient Incremental Large-Scale Orthophoto Generation via 3-D Gaussian Splatting SLAM

Abstract

This article presents a scalable and stable 3-D Gaussian splatting (3DGS)-simultaneous localization and mapping (SLAM) framework for efficient large-scale orthophoto generation. Unlike conventional SfM- or SLAM-based pipelines that rely on geometry-driven stitching, we reformulate orthophoto generation as a rendering-based mapping problem under a unified 3DGS-SLAM paradigm. However, directly applying 3DGS-SLAM to aerial mapping suffers from critical challenges, including uncontrolled memory growth, optimization instability, and slow convergence in large-scale UAV scenarios. To address these issues, we introduce a unified framework that jointly enforces memory-constrained representation, stabilized optimization, and accelerated convergence during incremental mapping. Specifically, we design an online gradient-driven mechanism to regulate Gaussian evolution, a viscous velocity regularization to stabilize optimization dynamics, and a geometry-aware homography-guided densification strategy to accelerate convergence under planar scene priors. Furthermore, by aligning GNSS with the SLAM system, our framework enables globally consistent orthographic rendering, producing geometrically and geographically consistent orthophotos in a unified coordinate frame. Extensive experiments on multisource UAV datasets, including those equipped with ground control points (GCPs), demonstrate that the proposed method achieves high absolute metric accuracy, along with superior efficiency, stability, and visual fidelity, enabling practical real-time incremental orthophoto generation for large-scale aerial environments.