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...
arXiv:2609.14634v1 Announce Type: new Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-wo...
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.
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.
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.
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:2607. 08408v1 Announce Type: cross Abstract: Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy.
Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video.
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
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.
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.
arXiv:2610.01744v1 Announce Type: new Abstract: Robot manipulation models primarily reason from 2D observations while acting in the 3D physical world. To bridge this gap, recent work has augmented ro...