GLAM-SLAM: Real-time Gaussian Large-scale Mapping via Flow Densification and Spatial Decomposition

• URL: http://arxiv.org/abs/2607.21416v1
• Date: Thu, 23 Jul 2026 15:22:42 GMT
• Title: GLAM-SLAM: Real-time Gaussian Large-scale Mapping via Flow Densification and Spatial Decomposition
• Abstract summary: We present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system.<n>We ensure lightweight tracking using a robust, feature-based SLAM.<n>We adopt a structured, sparse anchor grid representation that ensures scalable operation and maintains scene across long-term sequences.<n>We evaluate our system on the challenging, long-sequence KITTI Odometry, Oxford RobotCar, and M Splatalaga datasets.
• Score: 15.113183931728814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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