Online incremental orthophoto generation with multiple unmanned aerial vehicles (UAVs) remains challenging, as it requires accurate, efficient, and scalable mapping from distributed aerial observations. In this paper, we present a centralized GNSS-assisted multi-UAV 3D Gaussian Splatting SLAM framework for online incremental orthophoto mapping. Each UAV independently performs visual odometry to build local submaps, which are first aligned into a unified global coordinate system using GNSS constraints and further refined via inter-agent visual loop closures for improved cross-agent consistency. To enable scalable and high-quality mapping, we introduce two complementary Gaussian map maintenance modules: plane-guided grid-based collaborative densification, which improves mapping quality and accelerates convergence under multi-UAV conditions, and visibility-aware adaptive pruning, which effectively controls redundancy and memory usage. These components allow efficient joint optimization within a unified Gaussian representation. Experiments on multiple aerial datasets using video-derived image frames captured by consumer-grade UAV cameras demonstrate that the proposed system provides a favorable trade-off between geo-consistency, visual fidelity, and efficiency compared with existing methods. Quantitatively, the proposed method achieves a GCP RMSE of 2.32 m, completes multi-UAV orthophoto generation within 3.3–5.5 min, and reduces the total mapping time by approximately 35–55% compared with the corresponding single-UAV setting, while supporting online tracking and incremental orthophoto updates with bounded latency and memory consumption.
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.
Xiao Zhang, Hongbin Dong, Xiaozhou Zhu et al.· IEEE Transactions on Geoscie...· 0 citations
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