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.
arXiv:2609.21347v1 Announce Type: new Abstract: Recent progress in 3D Gaussian Splatting (3DGS) has enabled dense visual SLAM with pinhole cameras, yet most pipelines are not designed for panoramic i...
arXiv:2608.29003v1 Announce Type: cross Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...
arXiv:2609.17387v1 Announce Type: new Abstract: Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-chang...
CGS‑SLAM is a hybrid decentralized/centralized SLAM system that enables multi‑agent 3D Gaussian Splatting reconstruction using only RGB images and inertial data. Each agent locally tracks motion with inertial priors, builds a scaled map via a monocular depth estimator, and shares keyframe encodings to facilitate dynamic keyframing and submap alignment. A central server then aligns submaps with a view‑alignment model, keeping communication costs low while achieving competitive tracking, higher rendering quality, and accurate submap alignment in GNSS‑denied environments.
arXiv:2609.25746v1 Announce Type: cross Abstract: ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covarian...
RawSLAM introduces the first online Gaussian SLAM framework that operates directly on single‑exposure 16‑bit linear HDR images, overcoming the limitations of traditional 8‑bit LDR SLAM systems in extreme lighting. The approach combines an HDR Gaussian splatting module with a Reinhard‑compressed photometric objective and structure‑guided spatial weighting, achieving superior trajectory and reconstruction accuracy compared to a direct HDR adaptation of MonoGS. The same formulation also improves performance on standard 8‑bit inputs and can be transferred to other SLAM systems such as SplaTAM, Gaussian SLAM, and DROID‑W, eliminating tracking failures in challenging illumination sequences. Additionally, RawSLAM provides a new dataset of 10 real‑world indoor sequences with 16‑bit RAW imagery, depth, IMU, and OptiTrack poses.
VGGT-GS SLAM is a monocular 3D Gaussian Splatting SLAM system that operates on uncalibrated videos. It uses feed‑forward VGGT pose and depth priors to perform submap differentiable bundle adjustment, jointly refining camera poses, a 3D Gaussian map, and submap‑shared intrinsics and distortion parameters via analytic calibration Jacobians. The method introduces Gaussian‑native alignment for camera‑anchored scale refinement between submaps and loop‑closure verification, achieving improved localization accuracy and rendering quality on standard indoor benchmarks.
Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, and sparse, weakly overlapping viewpoints induced by forward rover motion and unconstrained driving directions. Under these conditions, state-of-the-art image-to-image and image-to-map matching pipelines suffer significant performance degradation.
arXiv:2606. 29237v1 Announce Type: cross Abstract: Robust robot autonomy depends on scene representations that remain stable enough to support localization, navigation, and downstream decision making in dynamic environments.
The paper introduces GRF-Recon, a framework for stable and scalable feed-forward 3D reconstruction from long monocular image sequences. It combines coarse-to-fine trajectory alignment, lightweight geometric prior injection via LoRA adaptation, and a hybrid-weight sparse ray-field optimization to refine local point clouds while enforcing cross-frame consistency. An efficient trajectory stitching strategy with joint ray-error optimization further reduces accumulated drift, achieving competitive trajectory accuracy compared to SLAM systems while maintaining globally consistent reconstructions in large-scale scenarios.
AMB3R‑SLAM is a real‑time monocular SLAM system that can reconstruct kilometer‑scale trajectories over 10,000 frames on a single consumer‑grade GPU. It combines a lightweight front‑end for low‑latency tracking with a hierarchical backend that enforces local, mid‑level, and global consistency, avoiding bundle adjustment and thus handling dynamic scenes naturally. The system also supports stereo, RGB‑D, and LiDAR inputs, achieving strong camera tracking performance and reducing absolute trajectory error by over 70% on several datasets, with sub‑meter accuracy when LiDAR is added.
arXiv:2609.23049v1 Announce Type: new Abstract: Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In rece...