2026· IEEE Transactions on Geoscience and Remote Sensing· Vol 64, pp. 3002319-3002319· 0 citations· 46 references
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.
This work revisits 3D Gaussian Splatting heuristics in a decoupled 3DGS-SLAM setting and proposes three geometry-aware methods that operate in the mapping thread: transmittance-preserving densification, camera-aware scale initialization from depth and intrinsics, and error-guided densification that focuses new primitives on high-residual regions.
Thai Luu, Quan Tran, Hieu Phan et al.· 0 citations
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibitive GPU memory requirements, limiting their applicability in realistic, long-horizon scenarios. To address this, we present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system designed for large-scale outdoor scenes. We ensure lightweight tracking using a robust, feature-based SLAM frontend, while for mapping, we adopt a structured, sparse anchor grid representation that ensures scalable operation and maintains scene coherence across long-term sequences. To satisfy the dense initialization requirements of 3D Gaussian Splatting (3DGS), we introduce a geometry-based flow-densification anchoring strategy using epipolar constraints. Furthermore, by treating mapping as a multi-scene problem, we propose a scene-partitioning strategy that introduces a strong spatial inductive bias via MLP initializations to generate localized Gaussians. We evaluate our system on the challenging, long-sequence KITTI Odometry, Oxford RobotCar, and M'alaga datasets. Extensive ablations and comparisons demonstrate a 15% improvement in reconstruction quality over the second-best performer, while maintaining real-time performance and the ability to scale to longer sequences. Code is publicly available for the benefit of the community.
This paper proposes LV-GS SLAM, a novel system that integrates LiDAR and visual data for incremental, large-scale reconstruction with real-time tracking, and develops a keyframe-based submap management framework that dynamically adjusts memory allocation based on both primitive density and inter-frame overlap ratio, effectively preventing GPU memory overflow.
Compared to state-of-the-art methods, the proposed FastPro-Gaussian achieves comparable TDOM fidelity with over two-fold acceleration in training time (notably more than two times faster than Tortho-GS), confirming its strong potential for practical deployment in geospatial production pipelines.
Chao Yang, Yapeng Li, Feiyang Liu et al.· Photogrammetric Engineering...· 0 citations
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.
SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. However, current 3DGS systems struggle to meet the strict demands of real-world deployments due to severe limitations in operational performance and map adaptability. To this end, we propose LightSplat, a hybrid-representation RGB-D SLAM framework. It synergizes local sparse features for robust and fast tracking with a dual-thread backend that progressively constructs dense Gaussian submaps. Crucially, we enable online loop closure through feature-accelerated 3DGS registration, refining overall map consistency through pose graph optimization. Ultimately, LightSplat achieves the online reconstruction of high-fidelity Gaussian map. Extensive experiments on multiple datasets and real-world robotic platform demonstrate that our method achieves near state-of-the-art reconstruction quality and the capability to accommodate practical camera motions, maintaining an average framerate of 8 FPS. Overall, LightSplat provides an efficient and robust foundation for deploying high-fidelity 3DGS in real-world environments.
Jun-Ze Bao, Ye Gao, Yi-Ming Huang et al.· 0 citations
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